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调整雷达到RTK标定分支为独立根目录结构

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.gitignore
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# Python
__pycache__/
*.pyc
*.py[cod]
.pytest_cache/
.venv/
venv/
# IDE / OS
.idea/
.vscode/
.DS_Store
Thumbs.db
# Raw data and generated outputs
data/raw/
work/
outputs/
*.rscap
*.dorec
*.log
# Large generated point clouds outside the archived reference result
**/frames/
**/frames_all/
CONSENSUS_SELECTION.md
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# 最终推荐值补充说明
两套后端各自结果均保留。最终推荐值又增加了一层不依赖外参X的交叉检查：只有同一运动对的Open3D B与small_gicp B相差不超过5 cm、0.5°时才进入最终求解；最终B数为39对。
最终推荐：
- 平移 `[1.297760, -0.000067, 0.720498] m`
- RPY `[-0.785151, 1.202661, -0.835510] deg`
- 第二批AX RMS：`0.07985 m / 0.96118°`
- 第一批22对辅助复核：`0.06067 m / 1.00298°`
完整结果见 `results/final_extrinsic_recommended.json`，选择摘要见 `results/final_summary.json`。执行完整 `run_all.ps1` 后，再执行 `run_consensus_finish.ps1` 可重建最终推荐结果。
LICENSE
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LiDAR_RTK_Direct_Calibration/.gitignore
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# Python
__pycache__/
*.py[cod]
.pytest_cache/
.venv/
venv/
# IDE / OS
.idea/
.vscode/
.DS_Store
Thumbs.db
# Raw data and generated outputs
data/raw/
work/
outputs/
*.rscap
*.dorec
*.log
# Large generated point clouds outside the archived reference result
**/frames/
**/frames_all/
LiDAR_RTK_Direct_Calibration/README.md
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# 双天线RTK—3D LiDAR直接手眼标定
本仓库从静态站点原始数据复现 `T_RTK_lidar`：把原始雷达坐标转换到RTK导航坐标系。它**不是** `base_link` 车体外参，也不会在求解阶段使用车体航向偏置或RTK到后轮轴的XY杆臂。
## 1. 输出坐标约定
统一约定 `T_A_B` 把B系点变换到A系：
```text
p_RTK = T_RTK_lidar · p_lidar
```
RTK导航系在本仓库中定义为：
- 原点：GGA位置参考点（通常为ANT1相位中心，必须结合接收机配置确认）；
- X轴：`rawHeading`所表示的双天线基线在水平面的投影；
- Y轴：左；
- Z轴：上；
- ENU航向：`yaw = 90° - rawHeading`；
- roll、pitch：当前轨迹中固定为0。
如果下游需要 `T_body_lidar`，必须另有经过确认的 `T_body_rtk`：
```text
T_body_lidar = T_body_rtk · T_RTK_lidar
```
## 2. 算法流程
```text
逐站LiDAR dlog + RTK.rscap + IMU.rscap
→ 分别解析并保留原始字段
→ 以LiDAR帧时间为索引关联RTK/IMU，生成combined NPZ
→ 每站选择一帧静态点云，GGA转局部ENU，rawHeading构造yaw-only RTK pose
→ Open3D GICP和small_gicp分别求 B_ij = T_Li_Lj
→ 留出点、Hessian、正反向、多初值和旋转共轭不变量筛选
→ 两后端共同认可的边形成consensus B
→ A_ij X = X B_ij + 地面法向/高度约束求 X = T_RTK_lidar
→ bootstrap、双后端差异、逐对残差和3D可视化检查
```
代码实际使用：
```text
A_ij = inv(T_W_Ri) · T_W_Rj = T_Ri_Rj
B_ij = T_Li_Lj # 将站点j点云变换到站点i
A_ij · X = X · B_ij
X = T_RTK_lidar
```
## 3. 原始数据目录
大体积数据不提交Git。`DataRoot`下每个站点必须是一个独立dlog目录，至少包含：
```text
raw_dataset/
├── stations/
│ ├── 001/
│ │ ├── dobject/
│ │ └── dobject_recording/
│ ├── 002/
│ └── ...
└── captures/
├── rtk.rscap
└── imu.rscap
```
每个站点应在车辆完全静止后记录点云；建议不少于30站，并包含充足的直行、左转、右转和大角度转向姿态变化。
## 4. 环境安装
已验证环境为Windows、PowerShell、Python 3.11。安装依赖：
```powershell
python -m pip install -r requirements.txt
```
依赖包括NumPy、SciPy、Open3D和small_gicp。若small_gicp没有对应Windows wheel，可在WSL2中安装后运行Python核心命令，或先只运行Open3D后端；完整共识流程需要两个后端都可用。
## 5. 从原始数据一键复现
在仓库根目录执行，路径由使用者通过参数传入，脚本内没有本机绝对路径：
```powershell
$Repo = (Resolve-Path ".").Path
$Raw = "E:\calibration_data\data4"
$Out = "E:\calibration_output\rtk_lidar"
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_full_pipeline.ps1" `
-DataRoot "$Raw\stations" `
-RtkCapture "$Raw\captures\rtk.rscap" `
-ImuCapture "$Raw\captures\imu.rscap" `
-OutputRoot $Out `
-RtkReferenceHeightAboveGroundM 0.8535 `
-ExpectedStations 34
```
主要输出：
```text
$Out/
├── exported/
│ ├── export/ # 各站LiDAR逐帧NPZ
│ ├── parsed/ # RTK/IMU JSONL
│ └── combined/ # 按LiDAR帧关联后的多传感器NPZ
├── prepared_rtk_direct/
│ ├── frames_all/ # 每站选中的静态帧
│ └── reference_poses_rtk_gga_raw_heading.csv
└── calibration/
├── open3d_gicp/
├── small_gicp/
├── consensus/
├── summary.json
└── final_T_RTK_lidar.json
```
若已经有`combined/`，可跳过原始导出：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_direct_rtk_lidar.ps1" `
-CombinedRoot "E:\calibration_output\exported\combined" `
-WorkRoot "E:\calibration_output\prepared_rtk_direct" `
-OutputRoot "E:\calibration_output\calibration" `
-RtkReferenceHeightAboveGroundM 0.8535 `
-ExpectedStations 34
```
## 6. 3D可视化
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_result.ps1" `
-Frames "$Out\prepared_rtk_direct\frames_all" `
-Pairs "$Out\calibration\consensus\B_consensus.npz" `
-Extrinsic "$Out\calibration\final_T_RTK_lidar.json" `
-PairIndex 0
```
窗口中：
- 蓝色：目标站点i；橙色：站点j；
- `1`：原始点云；
- `2`：RTK运动A直接作为初值；
- `3`：GICP测得的B；
- `4`：最终外参预测的 `X^-1 A X`；
- `Q/Esc`：退出。
模式3和4应让同一墙面、立柱、路缘和地面尽量重合。终端同时打印 `B^-1(X^-1AX)` 的平移和旋转增量。应查看多对，不能只挑视觉效果最好的一对。
## 7. data4参考结果
仓库保留了精简参考产物，见[`results/reference_data4`](results/reference_data4/README.md)：
```text
translation_m = [1.638179350, -0.240844799, 0.084481236]
RPY_deg_xyz = [-0.817167459, 1.323288119, -22.104163318]
站点：34
共识运动对：25
AX Translation RMS：0.100207 m
AX Rotation RMS：1.252794°
Weighted Jacobian condition：7.739413
Open3D vs small_gicp：0.003889 m / 0.188431°
```
唯一建议下游读取的参考结果是[`final_T_RTK_lidar.json`](results/reference_data4/final_T_RTK_lidar.json)。
## 8. z与精度限制
平面阿克曼运动不能独立观测z。参考结果使用34站地面平面和RTK参考点离地`0.8535 m`约束z；该高度必须量到实际GGA参考点/天线相位中心。更改参考高度后必须重新求解。
AX残差、Hessian/Jacobian条件数、bootstrap和双后端一致性只证明内部一致性，不能单独证明逐帧GT达到±3 cm。当前关联仍以LiDAR和串口主机接收时间为主；GNSS周/周内时间和IMU设备时间被保留，但没有联合估计时钟偏移与漂移。用于连续GT pose前，应补做严格设备时间同步和独立轨迹验证。
此外，代码无法单独证明GGA对应哪根物理天线、`rawHeading`是ANT1→ANT2还是ANT2→ANT1；必须用接收机配置、接线和现场运动实验确认。方向错误会导致RTK坐标系yaw相差约180°。
## 9. 仓库目录
| 目录 | 职责 |
|---|---|
| [`code/`](code/) | GICP、运动对质量评价、AX=XB求解、结果封装和3D可视化 |
| [`tools/`](tools/) | 原始dlog/rscap解析、按LiDAR帧关联及静态站点prepared生成 |
| [`run/`](run/) | PowerShell入口；所有数据和输出路径都通过参数传入 |
| [`results/reference_data4/`](results/reference_data4/) | 可提交Git的精简参考结果，不包含点云和本机过程目录 |
| `work/`、`outputs/` | 本地运行生成物，已由`.gitignore`排除 |
各代码文件职责见[`code/README.md`](code/README.md)，命令索引见[`run/README.md`](run/README.md)，工具说明见[`tools/README.md`](tools/README.md)。
LiDAR_RTK_Direct_Calibration/code/compare_extrinsics.py
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#!/usr/bin/env python3
"""Compare two homogeneous-extrinsic JSON files in parameter space and on SE(3)."""
import argparse
import json
from pathlib import Path
import numpy as np
from scipy.spatial.transform import Rotation
def main() -> int:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--reference", type=Path, required=True)
parser.add_argument("--candidate", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
args = parser.parse_args()
reference = json.loads(args.reference.read_text(encoding="utf-8-sig"))
candidate = json.loads(args.candidate.read_text(encoding="utf-8-sig"))
a = np.asarray(reference["matrix_4x4"], dtype=float)
b = np.asarray(candidate["matrix_4x4"], dtype=float)
delta = np.linalg.inv(a) @ b
result = {
"convention": "delta = inverse(reference) @ candidate",
"reference": str(args.reference.resolve()),
"candidate": str(args.candidate.resolve()),
"candidate_minus_reference_translation_xyz_m": (b[:3, 3] - a[:3, 3]).tolist(),
"candidate_minus_reference_rpy_xyz_deg": (
np.asarray(candidate["rotation_rpy_deg_xyz"], float)
- np.asarray(reference["rotation_rpy_deg_xyz"], float)
).tolist(),
"relative_translation_norm_m": float(np.linalg.norm(delta[:3, 3])),
"relative_rotation_deg": float(np.degrees(Rotation.from_matrix(delta[:3, :3]).magnitude())),
"relative_matrix_4x4": delta.tolist(),
}
args.output.parent.mkdir(parents=True, exist_ok=True)
args.output.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(result, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
LiDAR_RTK_Direct_Calibration/code/cross_backend_filter.py
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#!/usr/bin/env python3
"""Keep common A/B edges on which Open3D and small_gicp agree, without using X."""
import argparse
import json
from pathlib import Path
import numpy as np
from scipy.spatial.transform import Rotation
def key(meta):
return int(meta[0]), int(meta[1])
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--open3d-pairs", required=True)
parser.add_argument("--small-pairs", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--audit")
parser.add_argument("--max-translation", type=float, default=0.05)
parser.add_argument("--max-rotation", type=float, default=0.50)
parser.add_argument("--min-pairs", type=int, default=25)
args = parser.parse_args()
with np.load(args.open3d_pairs, allow_pickle=False) as source:
open_a = np.asarray(source["A"], float)
open_b = np.asarray(source["B"], float)
open_meta = np.asarray(source["meta"], float)
station_times = np.asarray(source["station_times"])
rtk_dt = np.asarray(source["rtk_nearest_dt_s"])
with np.load(args.small_pairs, allow_pickle=False) as source:
small = {key(meta): np.asarray(b, float)
for meta, b in zip(source["meta"], source["B"])}
keep, audit = [], []
for meta, b_open in zip(open_meta, open_b):
edge = key(meta)
if edge not in small:
audit.append({"i": edge[0], "j": edge[1], "accepted": False,
"reason": "not_in_small_gicp_refined"})
keep.append(False)
continue
delta = np.linalg.inv(b_open) @ small[edge]
translation = float(np.linalg.norm(delta[:3, 3]))
rotation = float(np.rad2deg(Rotation.from_matrix(delta[:3, :3]).magnitude()))
accepted = translation <= args.max_translation and rotation <= args.max_rotation
keep.append(accepted)
audit.append({
"i": edge[0], "j": edge[1],
"open3d_small_translation_m": translation,
"open3d_small_rotation_deg": rotation,
"accepted": accepted,
"reason": "" if accepted else "backend_disagreement",
})
keep = np.asarray(keep, bool)
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(
output, A=open_a[keep], B=open_b[keep], meta=open_meta[keep],
station_times=station_times, rtk_nearest_dt_s=rtk_dt,
backend=np.asarray("open3d_gicp_cross_backend_consensus"),
)
audit_path = Path(args.audit or output.with_suffix(".consensus.json"))
audit_path.write_text(json.dumps({
"selection_is_X_independent": True,
"B_source": "Open3D; small_gicp is used only as an agreement gate",
"max_translation_m": args.max_translation,
"max_rotation_deg": args.max_rotation,
"input_open3d_pairs": len(open_b),
"accepted_pairs": int(np.count_nonzero(keep)),
"pairs": audit,
}, ensure_ascii=False, indent=2), encoding="utf-8")
if np.count_nonzero(keep) < args.min_pairs:
raise RuntimeError(f"only {np.count_nonzero(keep)} consensus pairs")
print(json.dumps({"accepted_pairs": int(np.count_nonzero(keep)),
"output": str(output.resolve()), "audit": str(audit_path.resolve())}, indent=2))
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/code/refine_pairs.py
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@@ -1,87 +0,0 @@
#!/usr/bin/env python3
"""X-independent second-stage filter for stationary A/B pairs."""
import argparse
import json
from pathlib import Path
import numpy as np
from rigorous_calibration import read_pairs, rotation_angle_deg
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--pairs", required=True)
parser.add_argument("--quality-json", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--audit")
parser.add_argument("--min-pairs", type=int, default=25)
parser.add_argument("--min-inlier-ratio", type=float, default=0.70)
parser.add_argument("--max-inlier-rmse", type=float, default=0.13)
parser.add_argument("--max-rotation-invariant-error", type=float, default=0.75)
parser.add_argument("--reverse-translation-tolerance", type=float, default=0.05)
parser.add_argument("--reverse-rotation-tolerance", type=float, default=0.50)
args = parser.parse_args()
a_array, b_array, meta, _ = read_pairs(args.pairs)
quality = json.loads(Path(args.quality_json).read_text(encoding="utf-8-sig"))
reports = {(int(item["i"]), int(item["j"])): item for item in quality["pairs"]}
keep, audit = [], []
for a_ij, b_ij, item_meta in zip(a_array, b_array, meta):
key = (int(item_meta[0]), int(item_meta[1]))
report = reports[key]
heldout = report["heldout_symmetric"]
reverse = report["forward_reverse"]
invariant = abs(rotation_angle_deg(a_ij[:3, :3]) - rotation_angle_deg(b_ij[:3, :3]))
reasons = []
if heldout["inlier_ratio"] < args.min_inlier_ratio:
reasons.append("overlap_ratio")
if heldout["inlier_rmse_m"] is None or heldout["inlier_rmse_m"] > args.max_inlier_rmse:
reasons.append("heldout_rmse")
if invariant > args.max_rotation_invariant_error:
reasons.append("rotation_conjugacy_invariant")
if reverse["translation_m"] > args.reverse_translation_tolerance:
reasons.append("forward_reverse_translation")
if reverse["rotation_deg"] > args.reverse_rotation_tolerance:
reasons.append("forward_reverse_rotation")
accepted = not reasons
keep.append(accepted)
audit.append({
"i": key[0], "j": key[1], "heldout_inlier_ratio": heldout["inlier_ratio"],
"heldout_inlier_rmse_m": heldout["inlier_rmse_m"],
"rotation_invariant_error_deg": invariant,
"reverse_translation_m": reverse["translation_m"],
"reverse_rotation_deg": reverse["rotation_deg"],
"accepted": accepted, "rejection_reasons": reasons,
})
keep = np.asarray(keep, bool)
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
with np.load(args.pairs, allow_pickle=False) as source:
np.savez_compressed(
output, A=a_array[keep], B=b_array[keep], meta=meta[keep],
station_times=np.asarray(source["station_times"]),
rtk_nearest_dt_s=np.asarray(source["rtk_nearest_dt_s"]),
backend=np.asarray(source["backend"]),
)
audit_path = Path(args.audit or output.with_suffix(".refinement.json"))
audit_path.write_text(json.dumps({
"selection_is_X_independent": True,
"criteria": {
"min_inlier_ratio": args.min_inlier_ratio,
"max_inlier_rmse_m": args.max_inlier_rmse,
"max_rotation_invariant_error_deg": args.max_rotation_invariant_error,
"reverse_translation_tolerance_m": args.reverse_translation_tolerance,
"reverse_rotation_tolerance_deg": args.reverse_rotation_tolerance,
},
"input_pairs": len(keep), "accepted_pairs": int(np.count_nonzero(keep)),
"pairs": audit,
}, ensure_ascii=False, indent=2), encoding="utf-8")
if np.count_nonzero(keep) < args.min_pairs:
raise RuntimeError(f"only {np.count_nonzero(keep)} refined pairs; need {args.min_pairs}")
print(json.dumps({"input_pairs": len(keep), "accepted_pairs": int(np.count_nonzero(keep)),
"output": str(output.resolve()), "audit": str(audit_path.resolve())}, indent=2))
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/code/rigorous_calibration.py
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@@ -1,771 +0,0 @@
#!/usr/bin/env python3
"""Rigorous stationary LiDAR / reference-trajectory hand-eye calibration.
Convention: T_A_B maps points from frame B into frame A.
For this repository the reference frame is the RTK navigation frame.
X = T_RTK_lidar, A_ij = T_W_Ri^-1 T_W_Rj, B_ij = T_Li_Lj,
therefore A_ij X = X B_ij. Raw sensor-frame points_raw are used.
"""
from __future__ import annotations
import argparse
import csv
import json
import math
import time
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from scipy.optimize import least_squares
from scipy.spatial import cKDTree
def skew(v):
x, y, z = v
return np.array([[0.0, -z, y], [z, 0.0, -x], [-y, x, 0.0]])
def so3_exp(v):
angle = float(np.linalg.norm(v))
if angle < 1e-12:
return np.eye(3) + skew(v)
k = skew(np.asarray(v, float) / angle)
return np.eye(3) + math.sin(angle) * k + (1.0 - math.cos(angle)) * k @ k
def so3_log(rotation):
cosine = float(np.clip((np.trace(rotation) - 1.0) / 2.0, -1.0, 1.0))
angle = math.acos(cosine)
vee = np.array([
rotation[2, 1] - rotation[1, 2],
rotation[0, 2] - rotation[2, 0],
rotation[1, 0] - rotation[0, 1],
])
if angle < 1e-9:
return vee / 2.0
if abs(math.pi - angle) < 1e-5:
values, vectors = np.linalg.eigh((rotation + np.eye(3)) / 2.0)
return vectors[:, int(np.argmax(values))] * angle
return vee * angle / (2.0 * math.sin(angle))
def quat_to_rotation(q):
x, y, z, w = np.asarray(q, float) / np.linalg.norm(q)
return np.array([
[1-2*(y*y+z*z), 2*(x*y-z*w), 2*(x*z+y*w)],
[2*(x*y+z*w), 1-2*(x*x+z*z), 2*(y*z-x*w)],
[2*(x*z-y*w), 2*(y*z+x*w), 1-2*(x*x+y*y)],
])
def rotation_to_quat(rotation):
from scipy.spatial.transform import Rotation
return Rotation.from_matrix(rotation).as_quat()
def rpy_deg(rotation):
from scipy.spatial.transform import Rotation
return Rotation.from_matrix(rotation).as_euler("xyz", degrees=True).tolist()
def make_transform(translation, rotation):
transform = np.eye(4)
transform[:3, :3] = rotation
transform[:3, 3] = translation
return transform
def params_transform(params):
return make_transform(params[:3], so3_exp(params[3:]))
def inverse_transform(transform):
answer = np.eye(4)
answer[:3, :3] = transform[:3, :3].T
answer[:3, 3] = -answer[:3, :3] @ transform[:3, 3]
return answer
def transform_points(points, transform):
return points @ transform[:3, :3].T + transform[:3, 3]
def rotation_angle_deg(rotation):
return math.degrees(np.linalg.norm(so3_log(rotation)))
@dataclass
class PoseSeries:
time: np.ndarray
transforms: np.ndarray
def read_poses(path):
timestamps, transforms = [], []
with Path(path).open(encoding="utf-8-sig", newline="") as stream:
reader = csv.DictReader(stream)
required = ("time", "x", "y", "z", "qx", "qy", "qz", "qw")
missing = [key for key in required if key not in (reader.fieldnames or [])]
if missing:
raise ValueError(f"{path}: missing pose fields {missing}")
for row in reader:
timestamps.append(float(row["time"]))
translation = np.array([float(row[k]) for k in ("x", "y", "z")])
quaternion = np.array([float(row[k]) for k in ("qx", "qy", "qz", "qw")])
transforms.append(make_transform(translation, quat_to_rotation(quaternion)))
order = np.argsort(timestamps)
return PoseSeries(np.asarray(timestamps)[order], np.asarray(transforms)[order])
def nearest_pose(series, timestamp):
index = int(np.argmin(np.abs(series.time - timestamp)))
return series.transforms[index], float(abs(series.time[index] - timestamp))
def npz_files(root):
files = sorted(Path(root).rglob("*.npz"))
if not files:
raise FileNotFoundError(f"no NPZ files under {root}")
return files
def load_npz_xyz(path, min_range=1.0, max_range=50.0):
with np.load(path, allow_pickle=False) as data:
if "points_raw" not in data:
raise ValueError(f"{path}: points_raw is required; cart-frame points are forbidden")
raw = np.asarray(data["points_raw"], dtype=np.float64)
timestamp = float(np.ravel(data["unix_time_ns"])[0]) / 1e9
counter = int(np.ravel(data["frame_counter"])[0])
distance = raw[:, 0] * 0.001
azimuth = np.deg2rad(raw[:, 1])
altitude = np.deg2rad(raw[:, 2])
valid = (
np.isfinite(distance + azimuth + altitude)
& (distance >= min_range)
& (distance <= max_range)
)
distance, azimuth, altitude = distance[valid], azimuth[valid], altitude[valid]
xyz = np.column_stack((
distance * np.cos(altitude) * np.cos(azimuth),
distance * np.cos(altitude) * np.sin(azimuth),
distance * np.sin(altitude),
))
return timestamp, counter, xyz
def load_stations(root, min_range, max_range, z_min=None, z_max=None):
stations = []
for path in npz_files(root):
timestamp, counter, xyz = load_npz_xyz(path, min_range, max_range)
if z_min is not None:
xyz = xyz[(xyz[:, 2] >= z_min) & (xyz[:, 2] <= z_max)]
stations.append((timestamp, counter, path, xyz))
stations.sort(key=lambda item: item[0])
return stations
def split_holdout(points, fraction, phase):
stride = max(int(round(1.0 / fraction)), 2)
index = np.arange(len(points))
holdout = ((index + phase) % stride) == 0
return points[~holdout], points[holdout]
def make_o3d_cloud(points, voxel):
import open3d as o3d
cloud = o3d.geometry.PointCloud()
cloud.points = o3d.utility.Vector3dVector(np.asarray(points, float))
return cloud.voxel_down_sample(voxel)
def align_open3d(target, source, initial, voxels, correspondences, iterations):
import open3d as o3d
registration = o3d.pipelines.registration
estimate = registration.TransformationEstimationForGeneralizedICP()
criteria = registration.ICPConvergenceCriteria(max_iteration=iterations)
transform, stages = np.asarray(initial, float), []
final_target = final_source = final_answer = None
started = time.perf_counter()
for voxel, correspondence in zip(voxels, correspondences):
target_cloud = make_o3d_cloud(target, voxel)
source_cloud = make_o3d_cloud(source, voxel)
answer = registration.registration_generalized_icp(
source_cloud, target_cloud, correspondence, transform, estimate, criteria
)
transform = np.asarray(answer.transformation, float)
stages.append({
"voxel_m": voxel,
"max_correspondence_m": correspondence,
"fitness": float(answer.fitness),
"inlier_rmse_m": float(answer.inlier_rmse),
"target_points": len(target_cloud.points),
"source_points": len(source_cloud.points),
})
final_target, final_source, final_answer = target_cloud, source_cloud, answer
information = registration.get_information_matrix_from_point_clouds(
final_source, final_target, correspondences[-1], transform
)
inliers = int(round(float(final_answer.fitness) * len(final_source.points)))
return {
"transform": transform,
"hessian": np.asarray(information, float),
"converged": None,
"iterations": None,
"num_inliers": inliers,
"objective": float(final_answer.inlier_rmse ** 2 * max(inliers, 1)),
"elapsed_sec": time.perf_counter() - started,
"stages": stages,
}
def align_small_gicp(target, source, initial, voxels, correspondences, iterations, threads):
import small_gicp
transform, stages, result = np.asarray(initial, float), [], None
started = time.perf_counter()
for voxel, correspondence in zip(voxels, correspondences):
result = small_gicp.align(
np.ascontiguousarray(target),
np.ascontiguousarray(source),
transform,
registration_type="GICP",
downsampling_resolution=voxel,
max_correspondence_distance=correspondence,
num_threads=threads,
max_iterations=iterations,
rotation_epsilon=math.radians(0.005),
translation_epsilon=0.0005,
verbose=False,
)
transform = np.asarray(result.T_target_source, float)
stages.append({
"voxel_m": voxel,
"max_correspondence_m": correspondence,
"converged": bool(result.converged),
"iterations": int(result.iterations),
"num_inliers": int(result.num_inliers),
"objective": float(result.error),
})
return {
"transform": transform,
"hessian": np.asarray(result.H, float),
"converged": bool(result.converged),
"iterations": int(result.iterations),
"num_inliers": int(result.num_inliers),
"objective": float(result.error),
"elapsed_sec": time.perf_counter() - started,
"stages": stages,
}
def align_backend(backend, target, source, initial, args):
if backend == "open3d":
return align_open3d(
target, source, initial, args.voxels, args.correspondences, args.iterations
)
return align_small_gicp(
target, source, initial, args.voxels, args.correspondences,
args.iterations, args.threads
)
def symmetric_heldout_metrics(target_fit, target_holdout, source_fit, source_holdout,
transform, threshold):
transformed_source_fit = transform_points(source_fit, transform)
transformed_source_holdout = transform_points(source_holdout, transform)
forward = cKDTree(target_fit).query(transformed_source_holdout, workers=-1)[0]
reverse = cKDTree(transformed_source_fit).query(target_holdout, workers=-1)[0]
distances = np.concatenate((forward, reverse))
inliers = distances[distances <= threshold]
return {
"evaluated": int(len(distances)),
"inliers": int(len(inliers)),
"inlier_ratio": float(len(inliers) / max(len(distances), 1)),
"inlier_rmse_m": float(np.sqrt(np.mean(inliers**2))) if len(inliers) else None,
"median_m": float(np.median(distances)),
"p90_m": float(np.quantile(distances, 0.90)),
"p95_m": float(np.quantile(distances, 0.95)),
}
def hessian_metrics(hessian, characteristic_length=10.0):
hessian = 0.5 * (np.asarray(hessian, float) + np.asarray(hessian, float).T)
scale = np.diag([1.0 / characteristic_length] * 3 + [1.0] * 3)
scaled = scale.T @ hessian @ scale
values, vectors = np.linalg.eigh(scaled)
largest = max(float(np.max(np.abs(values))), np.finfo(float).eps)
positive = values[values > largest * 1e-9]
condition = float(positive[-1] / positive[0]) if len(positive) else float("inf")
return {
"native_order": ["rx_rad", "ry_rad", "rz_rad", "tx_m", "ty_m", "tz_m"],
"scaled_eigenvalues": values.tolist(),
"effective_rank": int(len(positive)),
"scaled_condition_number": condition,
"weakest_scaled_direction": vectors[:, int(np.argmin(values))].tolist(),
}
def transform_difference(reference, candidate):
delta = inverse_transform(reference) @ candidate
return {
"translation_m": float(np.linalg.norm(delta[:3, 3])),
"rotation_deg": rotation_angle_deg(delta[:3, :3]),
}
def loop_metrics(transforms):
loops = []
for (i, j), b_ij in transforms.items():
for (j2, k), b_jk in transforms.items():
if j2 != j or (i, k) not in transforms:
continue
loops.append(transform_difference(transforms[(i, k)], b_ij @ b_jk))
if not loops:
return {"count": 0}
translation = np.array([item["translation_m"] for item in loops])
rotation = np.array([item["rotation_deg"] for item in loops])
return {
"count": len(loops),
"translation_rms_m": float(np.sqrt(np.mean(translation**2))),
"translation_p95_m": float(np.quantile(translation, 0.95)),
"rotation_rms_deg": float(np.sqrt(np.mean(rotation**2))),
"rotation_p95_deg": float(np.quantile(rotation, 0.95)),
}
def cmd_ground(args):
stations = load_stations(args.frames, args.min_range, args.max_range)
rows = []
for timestamp, counter, _, xyz in stations:
roi = xyz[(xyz[:, 2] >= args.z_min) & (xyz[:, 2] <= args.z_max)]
if len(roi) < args.min_inliers:
continue
cloud = make_o3d_cloud(roi, args.voxel)
plane, indexes = cloud.segment_plane(
args.distance_threshold, 3, args.ransac_iterations
)
normal = np.asarray(plane[:3], float)
norm = np.linalg.norm(normal)
normal, distance = normal / norm, float(plane[3] / norm)
if distance < 0:
normal, distance = -normal, -distance
points = np.asarray(cloud.points)[indexes]
rms = float(np.sqrt(np.mean((points @ normal + distance) ** 2)))
if len(indexes) >= args.min_inliers and rms <= args.max_rms:
rows.append([timestamp, *normal, distance, len(indexes), rms, counter])
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
with output.open("w", encoding="utf-8", newline="") as stream:
writer = csv.writer(stream)
writer.writerow(["time", "nx", "ny", "nz", "d", "inliers", "rms_m", "frame_counter"])
writer.writerows(rows)
print(json.dumps({"planes": len(rows), "output": str(output.resolve())}, indent=2))
def cmd_pairs(args):
if len(args.voxels) != len(args.correspondences):
raise ValueError("--voxels and --correspondences must have equal lengths")
stations = load_stations(
args.frames, args.min_range, args.max_range, args.z_min, args.z_max
)
reference = read_poses(args.reference_poses)
if len(stations) < args.min_stations:
raise ValueError(f"need at least {args.min_stations} stations, got {len(stations)}")
reference_poses, reference_dt = [], []
for timestamp, _, _, xyz in stations:
if len(xyz) < args.min_roi_points:
raise ValueError(f"station at {timestamp} has only {len(xyz)} ROI points")
pose, dt = nearest_pose(reference, timestamp + args.time_offset)
reference_poses.append(pose)
reference_dt.append(dt)
reference_poses = np.asarray(reference_poses)
split = [split_holdout(station[3], args.holdout_fraction, i)
for i, station in enumerate(stations)]
rng = np.random.default_rng(args.seed)
accepted_a, accepted_b, accepted_meta, reports = [], [], [], []
accepted_transforms = {}
for i in range(len(stations)):
for j in range(i + args.min_gap, min(len(stations), i + args.max_gap + 1)):
a_ij = inverse_transform(reference_poses[i]) @ reference_poses[j]
translation = float(np.linalg.norm(a_ij[:2, 3]))
rotation = rotation_angle_deg(a_ij[:3, :3])
if translation < args.min_translation and rotation < args.min_rotation:
continue
initial_b = a_ij.copy() # X0=I; no measured extrinsic.
target_fit, target_holdout = split[i]
source_fit, source_holdout = split[j]
forward = align_backend(args.backend, target_fit, source_fit, initial_b, args)
heldout = symmetric_heldout_metrics(
target_fit, target_holdout, source_fit, source_holdout,
forward["transform"], args.evaluation_distance
)
hessian = hessian_metrics(forward["hessian"])
reverse_answer = align_backend(
args.backend, source_fit, target_fit, inverse_transform(initial_b), args
)
reverse = transform_difference(
forward["transform"], inverse_transform(reverse_answer["transform"])
)
multistart = []
for _ in range(args.multistart):
perturb = np.r_[
rng.normal(0.0, args.multistart_translation_sigma, 3),
np.deg2rad(rng.normal(0.0, args.multistart_rotation_sigma, 3)),
]
candidate = align_backend(
args.backend, target_fit, source_fit,
params_transform(perturb) @ initial_b, args
)
multistart.append(transform_difference(forward["transform"], candidate["transform"]))
stable = [
item["translation_m"] <= args.multistart_translation_tolerance
and item["rotation_deg"] <= args.multistart_rotation_tolerance
for item in multistart
]
success_rate = float(np.mean(stable)) if stable else 1.0
reasons = []
if forward["converged"] is False:
reasons.append("backend_not_converged")
if heldout["inlier_ratio"] < args.min_inlier_ratio:
reasons.append("heldout_inlier_ratio")
if heldout["inlier_rmse_m"] is None or heldout["inlier_rmse_m"] > args.max_inlier_rmse:
reasons.append("heldout_inlier_rmse")
if hessian["effective_rank"] < 6:
reasons.append("hessian_rank")
if hessian["scaled_condition_number"] > args.max_hessian_condition:
reasons.append("hessian_condition")
if reverse["translation_m"] > args.reverse_translation_tolerance:
reasons.append("forward_reverse_translation")
if reverse["rotation_deg"] > args.reverse_rotation_tolerance:
reasons.append("forward_reverse_rotation")
if success_rate < args.min_multistart_success:
reasons.append("multistart_instability")
accepted = not reasons
report = {
"i": i, "j": j,
"lidar_time_i": stations[i][0], "lidar_time_j": stations[j][0],
"frame_counter_i": stations[i][1], "frame_counter_j": stations[j][1],
"rtk_translation_m": translation, "rtk_rotation_deg": rotation,
"nearest_rtk_dt_i_s": reference_dt[i], "nearest_rtk_dt_j_s": reference_dt[j],
"initial_B_source": "X0=identity; B0=A (no measured extrinsic)",
"B_ij_4x4": forward["transform"].tolist(),
"backend": args.backend, "backend_converged": forward["converged"],
"backend_iterations": forward["iterations"],
"backend_num_inliers": forward["num_inliers"],
"backend_objective": forward["objective"],
"backend_elapsed_sec": forward["elapsed_sec"],
"multiscale_stages": forward["stages"],
"heldout_symmetric": heldout, "hessian": hessian,
"forward_reverse": reverse,
"multistart": {"runs": len(multistart), "success_rate": success_rate,
"deltas": multistart},
"accepted": accepted, "rejection_reasons": reasons,
}
reports.append(report)
print(f"{args.backend} {i:02d}->{j:02d} rmse={heldout['inlier_rmse_m']} "
f"ratio={heldout['inlier_ratio']:.3f} accepted={accepted}")
if accepted:
accepted_a.append(a_ij)
accepted_b.append(forward["transform"])
accepted_meta.append([i, j, stations[i][0], stations[j][0]])
accepted_transforms[(i, j)] = forward["transform"]
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
np.savez_compressed(
output, A=np.asarray(accepted_a), B=np.asarray(accepted_b),
meta=np.asarray(accepted_meta),
station_times=np.asarray([item[0] for item in stations]),
rtk_nearest_dt_s=np.asarray(reference_dt), backend=np.asarray(args.backend),
)
quality = {
"schema_version": 2,
"backend": args.backend,
"transform_convention": "B_ij=T_Li_Lj maps station j points into station i",
"raw_point_field": "points_raw",
"measured_extrinsic_used_as_initial": False,
"stations": len(stations), "candidate_pairs": len(reports),
"accepted_pairs": len(accepted_a),
"parameters": vars(args),
"accepted_loop_closure": loop_metrics(accepted_transforms),
"pairs": reports,
}
quality["parameters"].pop("func", None)
quality_path = Path(args.quality_json or output.with_suffix(".quality.json"))
quality_path.write_text(json.dumps(quality, ensure_ascii=False, indent=2), encoding="utf-8")
csv_path = Path(args.quality_csv or output.with_suffix(".quality.csv"))
with csv_path.open("w", encoding="utf-8", newline="") as stream:
fields = ["i", "j", "rtk_translation_m", "rtk_rotation_deg",
"heldout_inlier_ratio", "heldout_inlier_rmse_m",
"hessian_rank", "hessian_condition", "reverse_translation_m",
"reverse_rotation_deg", "multistart_success_rate", "accepted",
"rejection_reasons"]
writer = csv.DictWriter(stream, fieldnames=fields)
writer.writeheader()
for item in reports:
writer.writerow({
"i": item["i"], "j": item["j"],
"rtk_translation_m": item["rtk_translation_m"],
"rtk_rotation_deg": item["rtk_rotation_deg"],
"heldout_inlier_ratio": item["heldout_symmetric"]["inlier_ratio"],
"heldout_inlier_rmse_m": item["heldout_symmetric"]["inlier_rmse_m"],
"hessian_rank": item["hessian"]["effective_rank"],
"hessian_condition": item["hessian"]["scaled_condition_number"],
"reverse_translation_m": item["forward_reverse"]["translation_m"],
"reverse_rotation_deg": item["forward_reverse"]["rotation_deg"],
"multistart_success_rate": item["multistart"]["success_rate"],
"accepted": item["accepted"],
"rejection_reasons": ";".join(item["rejection_reasons"]),
})
if len(accepted_a) < args.min_pairs:
raise RuntimeError(f"only {len(accepted_a)} accepted pairs; need {args.min_pairs}")
print(json.dumps({
"backend": args.backend, "stations": len(stations),
"candidate_pairs": len(reports), "accepted_pairs": len(accepted_a),
"output": str(output.resolve()), "quality_json": str(quality_path.resolve()),
"loop": quality["accepted_loop_closure"],
}, indent=2))
def read_planes(path):
planes = []
with Path(path).open(encoding="utf-8-sig", newline="") as stream:
for row in csv.DictReader(stream):
normal = np.array([float(row[k]) for k in ("nx", "ny", "nz")])
norm = np.linalg.norm(normal)
normal, distance = normal / norm, float(row["d"]) / norm
if distance < 0:
normal, distance = -normal, -distance
planes.append([*normal, distance])
return np.asarray(planes)
def read_pairs(path):
with np.load(path, allow_pickle=False) as data:
return (np.asarray(data["A"], float), np.asarray(data["B"], float),
np.asarray(data["meta"], float), len(data["station_times"]))
def calibration_residual(params, a_array, b_array, planes, args):
x = params_transform(params)
values = []
for a_ij, b_ij in zip(a_array, b_array):
error = inverse_transform(a_ij @ x) @ x @ b_ij
values.extend((error[:3, 3] / args.translation_sigma).tolist())
values.extend((so3_log(error[:3, :3]) / math.radians(args.rotation_sigma)).tolist())
body_up = np.array([0.0, 0.0, 1.0])
for plane in planes:
normal_body = x[:3, :3] @ plane[:3]
values.extend((np.cross(normal_body, body_up) / args.plane_normal_sigma).tolist())
body_distance = plane[3] - float(normal_body @ x[:3, 3])
values.append((body_distance - args.reference_height) / args.plane_height_sigma)
return np.asarray(values)
def pair_metrics(a_array, b_array, x):
translation, rotation, rows = [], [], []
for index, (a_ij, b_ij) in enumerate(zip(a_array, b_array)):
predicted = inverse_transform(x) @ a_ij @ x
delta = inverse_transform(b_ij) @ predicted
t = float(np.linalg.norm(delta[:3, 3]))
r = rotation_angle_deg(delta[:3, :3])
translation.append(t); rotation.append(r)
rows.append({"pair_index": index, "translation_m": t, "rotation_deg": r})
translation, rotation = np.asarray(translation), np.asarray(rotation)
def stats(values):
return {
"rms": float(np.sqrt(np.mean(values**2))),
"median": float(np.median(values)),
"p90": float(np.quantile(values, 0.90)),
"p95": float(np.quantile(values, 0.95)),
"max": float(np.max(values)),
}
return {"pairs": len(rows), "translation_m": stats(translation),
"rotation_deg": stats(rotation), "per_pair": rows}
def solve_extrinsic(a_array, b_array, planes, args):
rng = np.random.default_rng(args.seed)
starts = [np.zeros(6)]
for _ in range(args.solver_multistart - 1):
starts.append(np.r_[
rng.normal(0.0, args.start_translation_sigma, 3),
np.deg2rad(rng.normal(0.0, args.start_rotation_sigma, 3)),
])
candidates = []
lower = np.r_[[-5.0] * 3, [-math.pi] * 3]
upper = np.r_[[5.0] * 3, [math.pi] * 3]
for start in starts:
answer = least_squares(
calibration_residual, np.clip(start, lower, upper),
args=(a_array, b_array, planes, args),
bounds=(lower, upper), loss="huber", f_scale=1.5,
x_scale="jac", max_nfev=args.max_nfev,
)
candidates.append(answer)
best = min(candidates, key=lambda item: item.cost)
return best, candidates
def cmd_calibrate(args):
a_array, b_array, meta, stations = read_pairs(args.pairs)
planes = read_planes(args.ground_planes)
best, candidates = solve_extrinsic(a_array, b_array, planes, args)
x = params_transform(best.x)
residual = calibration_residual(best.x, a_array, b_array, planes, args)
absolute = np.abs(residual)
weights = np.ones_like(residual)
weights[absolute > 1.5] = 1.5 / absolute[absolute > 1.5]
weighted_jacobian = best.jac * np.sqrt(weights)[:, None]
singular = np.linalg.svd(weighted_jacobian, compute_uv=False)
condition = float(singular[0] / max(singular[-1], 1e-15))
dof = max(len(residual) - 6, 1)
covariance = np.linalg.pinv(weighted_jacobian.T @ weighted_jacobian) * float(
np.sum(weights * residual**2) / dof
)
sigma = np.sqrt(np.maximum(np.diag(covariance), 0.0))
candidate_summary = []
for item in candidates:
candidate_x = params_transform(item.x)
candidate_summary.append({
"cost": float(item.cost), "success": bool(item.success),
**transform_difference(x, candidate_x),
})
bootstrap = []
rng = np.random.default_rng(args.seed + 1)
for _ in range(args.bootstrap):
indexes = rng.integers(0, len(a_array), len(a_array))
answer = least_squares(
calibration_residual, best.x,
args=(a_array[indexes], b_array[indexes], planes, args),
loss="huber", f_scale=1.5, x_scale="jac", max_nfev=args.max_nfev,
)
bootstrap.append(np.r_[answer.x[:3], rpy_deg(so3_exp(answer.x[3:]))])
bootstrap = np.asarray(bootstrap)
result = {
"schema_version": 2,
"success": bool(best.success),
"message": best.message,
"convention": "T_reference_lidar maps raw LiDAR points into the supplied reference frame",
"equation": "A_ij X = X B_ij",
"measured_extrinsic_used_as_initial": False,
"translation_m": x[:3, 3].tolist(),
"rotation_rpy_deg_xyz": rpy_deg(x[:3, :3]),
"quaternion_xyzw": rotation_to_quat(x[:3, :3]).tolist(),
"matrix_4x4": x.tolist(),
"estimation": {"stations": stations, "pairs": len(a_array),
"residuals": pair_metrics(a_array, b_array, x)},
"ground": {
"planes": len(planes),
"reference_origin_height_above_ground_m": args.reference_height,
"formula": "d_lidar - (R_X n_lidar)^T t_X - reference_height",
},
"linearized_one_sigma": {
"translation_m": sigma[:3].tolist(),
"rotation_deg": np.rad2deg(sigma[3:]).tolist(),
"warning": "conditional local estimate; bootstrap is the primary stability check",
},
"weighted_jacobian_condition_number": condition,
"solver_multistart": {
"runs": len(candidates), "candidates_relative_to_best": candidate_summary,
},
"bootstrap": {
"runs": len(bootstrap),
"order": ["x_m", "y_m", "z_m", "roll_deg", "pitch_deg", "yaw_deg"],
"std": np.std(bootstrap, axis=0, ddof=1).tolist() if len(bootstrap) > 1 else None,
"p025": np.quantile(bootstrap, 0.025, axis=0).tolist() if len(bootstrap) else None,
"p975": np.quantile(bootstrap, 0.975, axis=0).tolist() if len(bootstrap) else None,
},
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(result, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(result, ensure_ascii=False, indent=2))
def cmd_validate(args):
result = json.loads(Path(args.extrinsic).read_text(encoding="utf-8-sig"))
x = np.asarray(result["matrix_4x4"], float)
a_array, b_array, meta, stations = read_pairs(args.pairs)
metrics = pair_metrics(a_array, b_array, x)
for row, pair_meta in zip(metrics["per_pair"], meta):
row.update({"i": int(pair_meta[0]), "j": int(pair_meta[1])})
report = {
"role": "auxiliary check only; first-batch RTK is sparse",
"blind_with_respect_to_X": True,
"note": "No AX residual was used to select these pairs",
"stations": stations, "metrics": metrics,
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(report, ensure_ascii=False, indent=2))
def build_parser():
parser = argparse.ArgumentParser(description=__doc__)
commands = parser.add_subparsers(dest="command", required=True)
ground = commands.add_parser("ground")
ground.add_argument("--frames", required=True); ground.add_argument("--output", required=True)
ground.add_argument("--min-range", type=float, default=1.0); ground.add_argument("--max-range", type=float, default=30.0)
ground.add_argument("--z-min", type=float, default=-1.4); ground.add_argument("--z-max", type=float, default=-0.4)
ground.add_argument("--voxel", type=float, default=0.08); ground.add_argument("--distance-threshold", type=float, default=0.025)
ground.add_argument("--ransac-iterations", type=int, default=500); ground.add_argument("--min-inliers", type=int, default=500)
ground.add_argument("--max-rms", type=float, default=0.025); ground.set_defaults(func=cmd_ground)
pairs = commands.add_parser("pairs")
pairs.add_argument("--backend", choices=["open3d", "small_gicp"], required=True)
pairs.add_argument("--frames", required=True)
pairs.add_argument("--reference-poses", "--body", dest="reference_poses", required=True)
pairs.add_argument("--output", required=True); pairs.add_argument("--quality-json"); pairs.add_argument("--quality-csv")
pairs.add_argument("--time-offset", type=float, default=0.0)
pairs.add_argument("--min-stations", type=int, default=30); pairs.add_argument("--min-pairs", type=int, default=25)
pairs.add_argument("--min-gap", type=int, default=1); pairs.add_argument("--max-gap", type=int, default=5)
pairs.add_argument("--min-translation", type=float, default=0.5); pairs.add_argument("--min-rotation", type=float, default=3.0)
pairs.add_argument("--min-range", type=float, default=2.0); pairs.add_argument("--max-range", type=float, default=50.0)
pairs.add_argument("--z-min", type=float, default=-0.60); pairs.add_argument("--z-max", type=float, default=5.0)
pairs.add_argument("--min-roi-points", type=int, default=1000)
pairs.add_argument("--holdout-fraction", type=float, default=0.20)
pairs.add_argument("--voxels", nargs="+", type=float, default=[0.30, 0.15, 0.08])
pairs.add_argument("--correspondences", nargs="+", type=float, default=[1.20, 0.50, 0.25])
pairs.add_argument("--iterations", type=int, default=60); pairs.add_argument("--threads", type=int, default=8)
pairs.add_argument("--evaluation-distance", type=float, default=0.25)
pairs.add_argument("--min-inlier-ratio", type=float, default=0.35); pairs.add_argument("--max-inlier-rmse", type=float, default=0.16)
pairs.add_argument("--max-hessian-condition", type=float, default=1e8)
pairs.add_argument("--reverse-translation-tolerance", type=float, default=0.08)
pairs.add_argument("--reverse-rotation-tolerance", type=float, default=0.50)
pairs.add_argument("--multistart", type=int, default=2)
pairs.add_argument("--multistart-translation-sigma", type=float, default=0.30)
pairs.add_argument("--multistart-rotation-sigma", type=float, default=3.0)
pairs.add_argument("--multistart-translation-tolerance", type=float, default=0.08)
pairs.add_argument("--multistart-rotation-tolerance", type=float, default=0.50)
pairs.add_argument("--min-multistart-success", type=float, default=0.50)
pairs.add_argument("--seed", type=int, default=20260721); pairs.set_defaults(func=cmd_pairs)
calibrate = commands.add_parser("calibrate")
calibrate.add_argument("--pairs", required=True); calibrate.add_argument("--ground-planes", required=True)
calibrate.add_argument("--output", required=True)
calibrate.add_argument("--translation-sigma", type=float, default=0.05)
calibrate.add_argument("--rotation-sigma", type=float, default=0.5)
calibrate.add_argument("--plane-normal-sigma", type=float, default=0.02)
calibrate.add_argument("--plane-height-sigma", type=float, default=0.03)
calibrate.add_argument("--reference-height", "--body-height", dest="reference_height", type=float, default=0.8535)
calibrate.add_argument("--solver-multistart", type=int, default=12)
calibrate.add_argument("--start-translation-sigma", type=float, default=1.0)
calibrate.add_argument("--start-rotation-sigma", type=float, default=20.0)
calibrate.add_argument("--bootstrap", type=int, default=100)
calibrate.add_argument("--max-nfev", type=int, default=1000)
calibrate.add_argument("--seed", type=int, default=20260721); calibrate.set_defaults(func=cmd_calibrate)
validate = commands.add_parser("validate")
validate.add_argument("--pairs", required=True); validate.add_argument("--extrinsic", required=True)
validate.add_argument("--output", required=True); validate.set_defaults(func=cmd_validate)
return parser
def main():
args = build_parser().parse_args()
args.func(args)
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/code/visualize_pair_3d.py
-169
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@@ -1,169 +0,0 @@
#!/usr/bin/env python3
"""Interactive 3D comparison of raw, RTK, GICP and hand-eye-predicted motion."""
import argparse
import json
import numpy as np
from scipy.spatial.transform import Rotation
from rigorous_calibration import (
inverse_transform, load_stations, rotation_angle_deg, rpy_deg, transform_points,
)
COLORS = {
"target": [0.10, 0.65, 1.00],
"source": [1.00, 0.35, 0.05],
}
def cloud(o3d, points, color, voxel):
item = o3d.geometry.PointCloud()
item.points = o3d.utility.Vector3dVector(points)
item = item.voxel_down_sample(voxel)
item.paint_uniform_color(color)
return item
def delta_components(reference, candidate):
"""Components of reference^-1*candidate, plus coordinate-invariant norms."""
delta = inverse_transform(reference) @ candidate
translation = np.asarray(delta[:3, 3], float)
return {
"translation_xyz_cm": (translation * 100.0).tolist(),
"translation_norm_cm": float(np.linalg.norm(translation) * 100.0),
"rotation_rpy_deg_xyz": rpy_deg(delta[:3, :3]),
"rotation_angle_deg": rotation_angle_deg(delta[:3, :3]),
}
def body_left_rpy(x, rpy_correction_deg):
correction = np.eye(4)
correction[:3, :3] = Rotation.from_euler(
"xyz", np.asarray(rpy_correction_deg, float), degrees=True
).as_matrix()
return correction @ x
def print_delta(name, reference, candidate):
item = delta_components(reference, candidate)
tx, ty, tz = item["translation_xyz_cm"]
roll, pitch, yaw = item["rotation_rpy_deg_xyz"]
print(
f"{name}: B^-1*motion translation xyz = "
f"[{tx:+.4f}, {ty:+.4f}, {tz:+.4f}] cm; "
f"rpy xyz = [{roll:+.4f}, {pitch:+.4f}, {yaw:+.4f}] deg; "
f"norm = {item['translation_norm_cm']:.4f} cm / "
f"{item['rotation_angle_deg']:.6f} deg"
)
return item
def main():
import open3d as o3d
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--frames", required=True)
parser.add_argument("--pairs", required=True)
parser.add_argument("--extrinsic", required=True)
parser.add_argument("--pair-index", type=int, default=0)
parser.add_argument("--voxel", type=float, default=0.10)
parser.add_argument(
"--left-rpy-deg", nargs=3, type=float, default=[0.0, 0.0, 0.0],
metavar=("ROLL", "PITCH", "YAW"),
help="optional body-frame left correction applied as DeltaR_body * X",
)
args = parser.parse_args()
stations = load_stations(args.frames, 1.0, 60.0)
with np.load(args.pairs, allow_pickle=False) as data:
if len(stations) != len(data["station_times"]):
raise ValueError(
f"frames contain {len(stations)} stations but pair file records "
f"{len(data['station_times'])}"
)
if not 0 <= args.pair_index < len(data["A"]):
raise IndexError(
f"pair-index {args.pair_index} outside [0,{len(data['A']) - 1}]"
)
a_ij = np.asarray(data["A"][args.pair_index], float)
b_gicp = np.asarray(data["B"][args.pair_index], float)
i, j = np.asarray(data["meta"][args.pair_index, :2], int)
with open(args.extrinsic, encoding="utf-8-sig") as stream:
result = json.load(stream)
x = np.asarray(result["matrix_4x4"], float)
b_calibrated = inverse_transform(x) @ a_ij @ x
transforms = {
"1 raw": np.eye(4),
"2 RTK initial (X0=I)": a_ij,
"3 GICP B": b_gicp,
"4 calibrated X^-1 A X": b_calibrated,
}
correction = np.asarray(args.left_rpy_deg, float)
if np.any(np.abs(correction) > 0.0):
x_test = body_left_rpy(x, correction)
transforms[
f"5 test body-left RPY {correction.tolist()} deg"
] = inverse_transform(x_test) @ a_ij @ x_test
target = stations[i][3]
source = stations[j][3]
print(f"pair_index={args.pair_index}, station {i} <- {j}")
print("blue = target station i; orange = source station j after selected transform")
print("keys: 1 raw | 2 RTK initial | 3 GICP | 4 calibrated | 5 test correction | Q/Esc exit")
print(
"IMPORTANT: delta xyz/rpy are components of B^-1*(X^-1*A*X), expressed "
"in station-j LiDAR coordinates; screen-left/right depends on the 3D camera view."
)
baseline = print_delta("mode 4 minus mode 3", b_gicp, b_calibrated)
roll, pitch, yaw = np.abs(baseline["rotation_rpy_deg_xyz"])
if max(roll, pitch) > max(0.10, 2.0 * yaw):
print("diagnosis: roll/pitch components dominate yaw; do not prioritize yaw tuning for this pair.")
tx, ty, tz = np.abs(baseline["translation_xyz_cm"])
if tz > max(tx, ty):
print("diagnosis: the largest translation component is relative Z, not lateral XY.")
body_up = np.array([0.0, 0.0, 1.0])
if np.linalg.norm(a_ij[:3, :3] @ body_up - body_up) < 1e-8:
print(
"observability: this A preserves the body Z axis, so body-left X.z "
"translation is unobservable from this pair; use ground/external height constraints."
)
if "5 test body-left RPY " + str(correction.tolist()) + " deg" in transforms:
print_delta("mode 5 minus mode 3", b_gicp, list(transforms.values())[-1])
viewer = o3d.visualization.VisualizerWithKeyCallback()
viewer.create_window("Rigorous LiDAR registration inspection - 3D", 1400, 900)
target_cloud = cloud(o3d, target, COLORS["target"], args.voxel)
source_cloud = cloud(o3d, source, COLORS["source"], args.voxel)
viewer.add_geometry(target_cloud)
viewer.add_geometry(source_cloud)
axes = o3d.geometry.TriangleMesh.create_coordinate_frame(size=1.0)
viewer.add_geometry(axes)
current = np.eye(4)
def select(name):
def callback(vis):
nonlocal current
desired = transforms[name]
source_cloud.transform(desired @ inverse_transform(current))
current = desired
vis.update_geometry(source_cloud)
if name == "3 GICP B":
print(f"{name}: reference registration B; delta = 0")
else:
print_delta(name + " minus mode 3", b_gicp, desired)
return False
return callback
for key, name in zip((ord("1"), ord("2"), ord("3"), ord("4"), ord("5")), transforms):
viewer.register_key_callback(key, select(name))
viewer.get_render_option().background_color = np.array([0.02, 0.02, 0.02])
viewer.get_render_option().point_size = 2.0
viewer.run()
viewer.destroy_window()
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/requirements.txt
-4
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@@ -1,4 +0,0 @@
numpy>=1.26
scipy>=1.11
open3d>=0.18
small-gicp
LiDAR_RTK_Direct_Calibration/results/README.md
-5
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@@ -1,5 +0,0 @@
# results目录
`reference_data4/`是本仓库附带的精简参考结果。新的运行结果应写到仓库外目录或`outputs/`，不要覆盖参考结果。
参考结果保留最终矩阵、共识B、两后端精筛B、逐对CSV/筛选审计和地面平面；未保留原始点云、逐帧combined数据、冗长的初筛JSON和带本机绝对路径的过程文件。
LiDAR_RTK_Direct_Calibration/run/README.md
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# run目录
根README包含完整复现命令；这里仅列入口职责。
| 脚本 | 用途 |
|---|---|
| `run_full_pipeline.ps1` | 从逐站LiDAR dlog、RTK rscap、IMU rscap一直运行到最终`T_RTK_lidar` |
| `export_multisensor_stations.ps1` | 解析原始三传感器数据并按LiDAR帧生成combined NPZ |
| `prepare_multisensor_dataset.ps1` | 每站选一帧，生成yaw-only RTK参考轨迹和`frames_all` |
| `run_direct_rtk_lidar.ps1` | 从combined数据运行RTK直接标定和最终结果封装 |
| `run_single_dataset.ps1` | 执行地面、两个GICP后端、精筛、共识和AX=XB求解 |
| `view_result.ps1` | 打开3D运动对对比并打印数值增量 |
所有路径均为命令行参数；默认生成目录`work/`和`outputs/`不会提交Git。
LiDAR_RTK_Direct_Calibration/run/export_multisensor_stations.ps1
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param(
[Parameter(Mandatory = $true)][string]$DataRoot,
[Parameter(Mandatory = $true)][string]$OutputRoot,
[Parameter(Mandatory = $true)][string]$RtkCapture,
[Parameter(Mandatory = $true)][string]$ImuCapture,
[string]$LidarObject = "frontlidar",
[string]$Timezone = "+08:00",
[string[]]$StationNames = @(),
[int]$Stride = 1,
[double]$RtkMaxDtMs = 150.0,
[double]$ImuBeforeMs = 100.0,
[double]$ImuAfterMs = 100.0,
[switch]$SkipLidarExport,
[switch]$SkipSerialParsing
)
$ErrorActionPreference = "Stop"
$RepoRoot = Split-Path -Parent $PSScriptRoot
$Exporter = Join-Path $RepoRoot "tools\frontlidar_dlog_export.py"
$Builder = Join-Path $RepoRoot "tools\build_multisensor_npz.py"
$Parser = Join-Path $RepoRoot "tools\rscap_v2\parse_rtk_imu_v2.py"
$Auditor = Join-Path $RepoRoot "tools\rscap_v2\audit_capture_v2.py"
$ExportRoot = Join-Path $OutputRoot "export"
$ParsedRoot = Join-Path $OutputRoot "parsed"
$CombinedRoot = Join-Path $OutputRoot "combined"
function Run-Python {
param([string]$Stage, [string[]]$Arguments)
Write-Host "[$Stage]"
& python @Arguments
if ($LASTEXITCODE -ne 0) {
throw "$Stage failed with Python exit code $LASTEXITCODE"
}
}
foreach ($Path in @($DataRoot, $RtkCapture, $ImuCapture)) {
if (-not (Test-Path -LiteralPath $Path)) { throw "Input does not exist: $Path" }
}
if ($Stride -lt 1) { throw "Stride must be at least 1" }
if ($StationNames.Count -gt 0) {
$Stations = @($StationNames | ForEach-Object { Get-Item -LiteralPath (Join-Path $DataRoot $_) })
} else {
$Stations = @(Get-ChildItem -LiteralPath $DataRoot -Directory | Where-Object {
(Test-Path -LiteralPath (Join-Path $_.FullName "dobject")) -and
(Test-Path -LiteralPath (Join-Path $_.FullName "dobject_recording"))
} | Sort-Object Name)
}
if ($Stations.Count -eq 0) { throw "No station directory containing dobject and dobject_recording was found" }
New-Item -ItemType Directory -Force -Path $OutputRoot | Out-Null
if (-not $SkipSerialParsing) {
New-Item -ItemType Directory -Force -Path $ParsedRoot | Out-Null
Run-Python "capture audit" @($Auditor, $RtkCapture, $ImuCapture, "--out", (Join-Path $OutputRoot "capture_audit.json"))
Run-Python "RTK/IMU parse" @($Parser, "--rtk", $RtkCapture, "--imu", $ImuCapture, "--out", $ParsedRoot)
}
foreach ($Station in $Stations) {
$StationOut = Join-Path $ExportRoot $Station.Name
if (-not $SkipLidarExport) {
Run-Python "LiDAR station $($Station.Name)" @(
$Exporter, "--dlog", $Station.FullName, "--out", $StationOut,
"--object", $LidarObject, "--format", "npz", "--timezone", $Timezone,
"--stride", "$Stride", "--compress", "--skip-rtk", "--write-reports", "--resume"
)
}
if (-not (Test-Path -LiteralPath (Join-Path $StationOut "frames"))) {
throw "Exported frame directory is absent for station $($Station.Name): $StationOut"
}
}
$BuildArgs = @($Builder)
foreach ($Station in $Stations) {
$Frames = Join-Path (Join-Path $ExportRoot $Station.Name) "frames"
$BuildArgs += @("--lidar", "$($Station.Name)=$Frames")
}
$BuildArgs += @(
"--rtk", (Join-Path $ParsedRoot "rtk.jsonl"),
"--imu", (Join-Path $ParsedRoot "imu.jsonl"),
"--out", $CombinedRoot,
"--rtk-max-dt-ms", "$RtkMaxDtMs",
"--imu-before-ms", "$ImuBeforeMs",
"--imu-after-ms", "$ImuAfterMs",
"--overwrite"
)
Run-Python "LiDAR/RTK/IMU association" $BuildArgs
Write-Host "Completed stations: $($Stations.Count)"
Write-Host "Combined NPZ: $CombinedRoot"
LiDAR_RTK_Direct_Calibration/run/prepare_multisensor_dataset.ps1
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param(
[Parameter(Mandatory = $true)][string]$CombinedRoot,
[Parameter(Mandatory = $true)][string]$Output,
[Parameter(Mandatory = $true)][double]$HeadingOffsetDeg,
[Parameter(Mandatory = $true)][double[]]$AntennaLever,
[string]$PoseName = "rtk_gga_raw_heading",
[int]$MinStations = 30,
[int]$ExpectedStations = 0,
[double]$HeadingStdLimitDeg = 0.5,
[switch]$Overwrite
)
$ErrorActionPreference = "Stop"
if ($AntennaLever.Count -ne 3) { throw "AntennaLever must contain X,Y,Z in body coordinates" }
$Repo = Split-Path -Parent $PSScriptRoot
$Args = @((Join-Path $Repo "tools\prepare_multisensor_station_dataset.py"), "--combined-root", $CombinedRoot,
"--output", $Output, "--pose-name", $PoseName, "--heading-offset-deg", "$HeadingOffsetDeg", "--antenna-lever") +
@($AntennaLever | ForEach-Object { "$_" }) + @("--min-stations", "$MinStations",
"--expected-stations", "$ExpectedStations", "--heading-std-limit-deg", "$HeadingStdLimitDeg")
if ($Overwrite) { $Args += "--overwrite" }
& python @Args
if ($LASTEXITCODE -ne 0) { throw "Multisensor dataset preparation failed" }
LiDAR_RTK_Direct_Calibration/run/run_single_dataset.ps1
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param(
[Parameter(Mandatory = $true)][string]$Prepared,
[Parameter(Mandatory = $true)][string]$OutputRoot,
[string]$ReferencePoseFile = "reference_poses_rtk_gga_raw_heading.csv",
[double]$ReferenceHeight = 0.8535,
[int]$MinPairs = 20,
[int]$Bootstrap = 100
)
$ErrorActionPreference = "Stop"
$Repo = Split-Path -Parent $PSScriptRoot
$Code = Join-Path $Repo "code\rigorous_calibration.py"
$Refine = Join-Path $Repo "code\refine_pairs.py"
$Consensus = Join-Path $Repo "code\cross_backend_filter.py"
$Frames = Join-Path $Prepared "frames_all"
$ReferencePoses = Join-Path $Prepared $ReferencePoseFile
$Common = Join-Path $OutputRoot "common"
$Open = Join-Path $OutputRoot "open3d_gicp"
$Small = Join-Path $OutputRoot "small_gicp"
$ConsensusOut = Join-Path $OutputRoot "consensus"
function Run-Python {
param([string]$Stage, [string[]]$Arguments)
Write-Host "[$Stage]"
& python @Arguments
if ($LASTEXITCODE -ne 0) { throw "$Stage failed with Python exit code $LASTEXITCODE" }
}
foreach ($Path in @($Frames, $ReferencePoses)) {
if (-not (Test-Path -LiteralPath $Path)) { throw "Input does not exist: $Path" }
}
New-Item -ItemType Directory -Force -Path $Common,$Open,$Small,$ConsensusOut | Out-Null
$Ground = Join-Path $Common "ground_planes.csv"
Run-Python "ground planes" @($Code, "ground", "--frames", $Frames, "--output", $Ground)
foreach ($Backend in @("small_gicp", "open3d")) {
$Directory = if ($Backend -eq "small_gicp") { $Small } else { $Open }
$Raw = Join-Path $Directory "B_estimation.npz"
$QualityJson = Join-Path $Directory "B_quality.json"
$QualityCsv = Join-Path $Directory "B_quality.csv"
$PairArgs = @($Code, "pairs", "--backend", $Backend, "--frames", $Frames, "--reference-poses", $ReferencePoses,
"--output", $Raw, "--quality-json", $QualityJson, "--quality-csv", $QualityCsv,
"--min-pairs", "$MinPairs")
if ($Backend -eq "open3d") { $PairArgs += @("--max-gap", "3", "--multistart", "1", "--iterations", "40") }
Run-Python "$Backend pairs" $PairArgs
Run-Python "$Backend X-independent refinement" @(
$Refine, "--pairs", $Raw, "--quality-json", $QualityJson,
"--output", (Join-Path $Directory "B_refined.npz"), "--min-pairs", "$MinPairs"
)
Run-Python "$Backend calibration" @(
$Code, "calibrate", "--pairs", (Join-Path $Directory "B_refined.npz"),
"--ground-planes", $Ground, "--reference-height", "$ReferenceHeight",
"--bootstrap", "$Bootstrap", "--output", (Join-Path $Directory "extrinsic.json")
)
}
$ConsensusPairs = Join-Path $ConsensusOut "B_consensus.npz"
Run-Python "cross-backend consensus" @(
$Consensus, "--open3d-pairs", (Join-Path $Open "B_refined.npz"),
"--small-pairs", (Join-Path $Small "B_refined.npz"),
"--output", $ConsensusPairs, "--min-pairs", "$MinPairs"
)
Run-Python "consensus calibration" @(
$Code, "calibrate", "--pairs", $ConsensusPairs, "--ground-planes", $Ground,
"--reference-height", "$ReferenceHeight", "--bootstrap", "$Bootstrap",
"--output", (Join-Path $ConsensusOut "extrinsic.json")
)
Write-Host "Calibration results: $OutputRoot"
LiDAR_RTK_Direct_Calibration/run/view_result.ps1
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param(
[Parameter(Mandatory = $true)][string]$Frames,
[Parameter(Mandatory = $true)][string]$Pairs,
[Parameter(Mandatory = $true)][string]$Extrinsic,
[int]$PairIndex = 0,
[double]$LeftRollDeg = 0.0,
[double]$LeftPitchDeg = 0.0,
[double]$LeftYawDeg = 0.0
)
$ErrorActionPreference = "Stop"
$Repo = Split-Path -Parent $PSScriptRoot
foreach ($Path in @($Frames, $Pairs, $Extrinsic)) {
if (-not (Test-Path -LiteralPath $Path)) { throw "Input does not exist: $Path" }
}
& python (Join-Path $Repo "code\visualize_pair_3d.py") `
--frames $Frames --pairs $Pairs --extrinsic $Extrinsic --pair-index $PairIndex `
--left-rpy-deg $LeftRollDeg $LeftPitchDeg $LeftYawDeg
if ($LASTEXITCODE -ne 0) { throw "Visualization failed with Python exit code $LASTEXITCODE" }
LiDAR_RTK_Direct_Calibration/tools/README.md
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# tools目录
| 文件 | 输入→输出 |
|---|---|
| `frontlidar_dlog_export.py` | LiDAR dlog → 逐帧原始点云NPZ；时间来自DObject post tick |
| `rscap_v2/parse_rtk_imu_v2.py` | RTK/IMU rscap → JSONL，保存校验状态、主机时间、GNSS/IMU设备字段和原始报文 |
| `rscap_v2/audit_capture_v2.py` | 检查rscap结构、时间范围和记录统计 |
| `build_multisensor_npz.py` | 按LiDAR帧最近邻关联GGA/heading，并附加IMU时间窗 → combined NPZ |
| `prepare_multisensor_station_dataset.py` | combined NPZ → 每站一帧`frames_all`和`reference_poses_*.csv` |
当前标定只使用LiDAR和RTK；IMU保持原始传感器坐标，不参与点云去畸变或外参求解。prepared阶段对站内有效RTK取平均、对heading取圆均值，并选择有效帧序列的中间LiDAR帧。
LiDAR_RTK_Direct_Calibration/tools/build_multisensor_npz.py
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#!/usr/bin/env python3
"""Build one LiDAR-centric NPZ per frame with matched RTK and an IMU window.
Inputs are LiDAR frame NPZ files from frontlidar_dlog_export.py and parsed
RTK/IMU JSONL files from parse_rtk_imu_v2.py. Raw .rscap files remain the
traceability source; this script never modifies them.
"""
from __future__ import annotations
import argparse
import csv
import json
from pathlib import Path
from typing import Any
import numpy as np
GPS_EPOCH_UNIX_NS = 315964800 * 1_000_000_000
def parse_named_path(text: str) -> tuple[str, Path]:
if "=" not in text:
raise argparse.ArgumentTypeError("expected NAME=PATH")
name, raw_path = text.split("=", 1)
if not name.strip():
raise argparse.ArgumentTypeError("segment name is empty")
return name.strip(), Path(raw_path)
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser()
parser.add_argument(
"--lidar",
type=parse_named_path,
action="append",
required=True,
metavar="NAME=FRAMES_DIR",
help="Repeat for each LiDAR segment; directory contains exported *.npz frames.",
)
parser.add_argument("--rtk", type=Path, action="append", required=True, help="Parsed rtk.jsonl; repeat per session.")
parser.add_argument("--imu", type=Path, action="append", required=True, help="Parsed imu.jsonl; repeat per session.")
parser.add_argument("--out", type=Path, required=True)
parser.add_argument("--rtk-max-dt-ms", type=float, default=150.0)
parser.add_argument("--imu-before-ms", type=float, default=100.0)
parser.add_argument("--imu-after-ms", type=float, default=100.0)
parser.add_argument("--gps-utc-leap-seconds", type=int, default=18)
parser.add_argument("--overwrite", action="store_true")
return parser.parse_args()
def load_jsonl(paths: list[Path]) -> list[dict[str, Any]]:
rows: list[dict[str, Any]] = []
for source_index, path in enumerate(paths):
source_file = str(path.resolve())
with path.open("r", encoding="utf-8") as stream:
for line_number, line in enumerate(stream, start=1):
if not line.strip():
continue
row = json.loads(line)
row["_source_file"] = source_file
row["_source_index"] = source_index
row["_source_line"] = line_number
rows.append(row)
return rows
def utf8_array(value: Any) -> np.ndarray:
return np.frombuffer(str(value if value is not None else "").encode("utf-8"), dtype=np.uint8)
def scalar(array: np.ndarray) -> Any:
return array.reshape(-1)[0].item()
def nearest_index(times: np.ndarray, target: int) -> int:
if not len(times):
return -1
right = int(np.searchsorted(times, target, side="left"))
candidates = [index for index in (right - 1, right) if 0 <= index < len(times)]
return min(candidates, key=lambda index: abs(int(times[index]) - target))
def estimate_imu_times(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
"""Recover 100 Hz timing inside each serial chunk from device timestamps.
A capture chunk has one host receive timestamp but may contain several IMU
frames. The last frame is anchored to the chunk receive time and earlier
frames are moved backwards by their device timestamp difference.
"""
groups: dict[tuple[int, int], list[dict[str, Any]]] = {}
for row in rows:
if not row.get("crc_valid") or row.get("device_timestamp_ms") is None:
continue
key = (int(row["_source_index"]), int(row.get("source_chunk_sequence_last", -1)))
groups.setdefault(key, []).append(row)
result: list[dict[str, Any]] = []
for group in groups.values():
group.sort(key=lambda row: (int(row["device_timestamp_ms"]), int(row["_source_line"])))
last_device = int(group[-1]["device_timestamp_ms"])
host_ns = int(group[-1]["host_receive_utc_ns"])
for row in group:
delta_ms = (last_device - int(row["device_timestamp_ms"])) & 0xFFFFFFFF
if delta_ms > 60_000:
delta_ms = 0
copied = dict(row)
copied["estimated_time_ns"] = host_ns - delta_ms * 1_000_000
result.append(copied)
result.sort(key=lambda row: int(row["estimated_time_ns"]))
return result
def gnss_utc_ns(row: dict[str, Any], leap_seconds: int) -> int | None:
week, tow_ms = row.get("gnss_week"), row.get("gnss_tow_ms")
if week is None or tow_ms is None:
return None
seconds = int(week) * 604800 + float(tow_ms) / 1000.0 - leap_seconds
return GPS_EPOCH_UNIX_NS + int(round(seconds * 1_000_000_000))
def numeric_array(rows: list[dict[str, Any]], key: str, dtype: Any, default: Any) -> np.ndarray:
return np.asarray([row.get(key, default) if row.get(key) is not None else default for row in rows], dtype=dtype)
def raw_frame_matrix(rows: list[dict[str, Any]]) -> tuple[np.ndarray, np.ndarray]:
frames = [bytes.fromhex(str(row.get("raw_frame_hex", ""))) for row in rows]
lengths = np.asarray([len(frame) for frame in frames], dtype=np.int32)
width = max(lengths, default=0)
matrix = np.zeros((len(frames), width), dtype=np.uint8)
for index, frame in enumerate(frames):
matrix[index, : len(frame)] = np.frombuffer(frame, dtype=np.uint8)
return matrix, lengths
def add_rtk(values: dict[str, np.ndarray], prefix: str, row: dict[str, Any] | None, dt_ns: int | None) -> None:
values[f"{prefix}_valid"] = np.asarray([row is not None], dtype=np.uint8)
values[f"{prefix}_dt_ns"] = np.asarray([dt_ns or 0], dtype=np.int64)
values[f"{prefix}_host_receive_utc_ns"] = np.asarray([0], dtype=np.int64)
values[f"{prefix}_raw_utf8"] = utf8_array("")
values[f"{prefix}_source_file_utf8"] = utf8_array("")
values[f"{prefix}_source_raw_file_offset"] = np.asarray([-1], dtype=np.int64)
values[f"{prefix}_source_raw_byte_length"] = np.asarray([0], dtype=np.int32)
if row is None:
return
values[f"{prefix}_host_receive_utc_ns"] = np.asarray([row.get("host_receive_utc_ns", 0)], dtype=np.int64)
values[f"{prefix}_raw_utf8"] = utf8_array(row.get("raw_line", ""))
values[f"{prefix}_source_file_utf8"] = utf8_array(row.get("_source_file", ""))
values[f"{prefix}_source_raw_file_offset"] = np.asarray([row.get("source_raw_file_offset", -1)], dtype=np.int64)
values[f"{prefix}_source_raw_byte_length"] = np.asarray([row.get("source_raw_byte_length", 0)], dtype=np.int32)
def initialize_rtk_measurements(values: dict[str, np.ndarray]) -> None:
for key, dtype, default in (
("lat_deg", np.float64, np.nan), ("lon_deg", np.float64, np.nan),
("altitude_m", np.float64, np.nan), ("hdop", np.float64, np.nan),
("fix_quality", np.int32, -1), ("gga_satellites", np.int32, -1),
("differential_age_s", np.float64, np.nan),
("gnss_week", np.int32, -1), ("gnss_tow_ms", np.int64, -1),
("baseline_length_m", np.float64, np.nan), ("raw_heading_deg", np.float64, np.nan),
("pitch_deg", np.float64, np.nan), ("heading_stddev_deg", np.float64, np.nan),
("pitch_stddev_deg", np.float64, np.nan), ("heading_satellites", np.int32, -1),
("solution_satellites", np.int32, -1),
):
values[f"rtk_{key}"] = np.asarray([default], dtype=dtype)
values["rtk_fixed"] = np.asarray([0], dtype=np.uint8)
values["rtk_heading_solution_utf8"] = utf8_array("")
values["rtk_heading_gnss_utc_ns"] = np.asarray([0], dtype=np.int64)
values["rtk_heading_host_minus_gnss_ns"] = np.asarray([0], dtype=np.int64)
def main() -> int:
args = parse_args()
if args.out.exists() and any(args.out.iterdir()) and not args.overwrite:
raise FileExistsError(f"{args.out} is non-empty; pass --overwrite")
frames_out = args.out / "frames"
frames_out.mkdir(parents=True, exist_ok=True)
rtk_rows = load_jsonl(args.rtk)
gga = sorted(
[row for row in rtk_rows if row.get("type") == "GGA" and row.get("checksum_valid") and row.get("lat_deg") is not None],
key=lambda row: int(row["host_receive_utc_ns"]),
)
heading = sorted(
[row for row in rtk_rows if row.get("type") == "UNIHEADINGA" and row.get("checksum_valid") and row.get("heading_valid")],
key=lambda row: int(row["host_receive_utc_ns"]),
)
imu = estimate_imu_times(load_jsonl(args.imu))
gga_times = np.asarray([int(row["host_receive_utc_ns"]) for row in gga], dtype=np.int64)
heading_times = np.asarray([int(row["host_receive_utc_ns"]) for row in heading], dtype=np.int64)
imu_times = np.asarray([int(row["estimated_time_ns"]) for row in imu], dtype=np.int64)
manifest: list[dict[str, Any]] = []
global_index = 0
max_rtk_ns = int(args.rtk_max_dt_ms * 1_000_000)
before_ns = int(args.imu_before_ms * 1_000_000)
after_ns = int(args.imu_after_ms * 1_000_000)
for segment_name, frame_dir in args.lidar:
frame_paths = sorted(frame_dir.glob("*.npz"))
if not frame_paths:
raise FileNotFoundError(f"no NPZ frames under {frame_dir}")
for segment_index, source in enumerate(frame_paths):
with np.load(source, allow_pickle=False) as frame:
values = {key: np.asarray(frame[key]) for key in frame.files}
lidar_time_ns = int(scalar(values["unix_time_ns"]))
gga_index = nearest_index(gga_times, lidar_time_ns)
heading_index = nearest_index(heading_times, lidar_time_ns)
gga_row = gga[gga_index] if gga_index >= 0 else None
heading_row = heading[heading_index] if heading_index >= 0 else None
gga_dt = int(gga_times[gga_index]) - lidar_time_ns if gga_index >= 0 else None
heading_dt = int(heading_times[heading_index]) - lidar_time_ns if heading_index >= 0 else None
gga_ok = gga_row is not None and abs(gga_dt or 0) <= max_rtk_ns
heading_ok = heading_row is not None and abs(heading_dt or 0) <= max_rtk_ns
add_rtk(values, "rtk_gga", gga_row if gga_ok else None, gga_dt)
add_rtk(values, "rtk_heading", heading_row if heading_ok else None, heading_dt)
initialize_rtk_measurements(values)
if gga_ok and gga_row:
for key, dtype, default in (
("lat_deg", np.float64, np.nan), ("lon_deg", np.float64, np.nan),
("altitude_m", np.float64, np.nan), ("hdop", np.float64, np.nan),
("fix_quality", np.int32, -1), ("gga_satellites", np.int32, -1),
("differential_age_s", np.float64, np.nan),
):
values[f"rtk_{key}"] = np.asarray([gga_row.get(key, default)], dtype=dtype)
values["rtk_gga_satellites"] = np.asarray([gga_row.get("satellites", -1)], dtype=np.int32)
values["rtk_fixed"] = np.asarray([int(gga_row.get("fix_quality", -1)) in {4, 5}], dtype=np.uint8)
if heading_ok and heading_row:
for key, dtype, default in (
("gnss_week", np.int32, -1), ("gnss_tow_ms", np.int64, -1),
("baseline_length_m", np.float64, np.nan), ("raw_heading_deg", np.float64, np.nan),
("pitch_deg", np.float64, np.nan), ("heading_stddev_deg", np.float64, np.nan),
("pitch_stddev_deg", np.float64, np.nan),
("solution_satellites", np.int32, -1),
):
values[f"rtk_{key}"] = np.asarray([heading_row.get(key, default)], dtype=dtype)
values["rtk_heading_satellites"] = np.asarray([heading_row.get("satellites", -1)], dtype=np.int32)
values["rtk_heading_solution_utf8"] = utf8_array(heading_row.get("heading_solution", ""))
device_ns = gnss_utc_ns(heading_row, args.gps_utc_leap_seconds)
values["rtk_heading_gnss_utc_ns"] = np.asarray([device_ns or 0], dtype=np.int64)
values["rtk_heading_host_minus_gnss_ns"] = np.asarray(
[int(heading_row["host_receive_utc_ns"]) - device_ns if device_ns is not None else 0], dtype=np.int64
)
left = int(np.searchsorted(imu_times, lidar_time_ns - before_ns, side="left"))
right = int(np.searchsorted(imu_times, lidar_time_ns + after_ns, side="right"))
window = imu[left:right]
values["imu_window_count"] = np.asarray([len(window)], dtype=np.int32)
values["imu_valid"] = np.asarray([bool(window)], dtype=np.uint8)
values["imu_time_ns"] = numeric_array(window, "estimated_time_ns", np.int64, 0)
values["imu_host_receive_utc_ns"] = numeric_array(window, "host_receive_utc_ns", np.int64, 0)
for key in ("device_timestamp_ms", "pps_sync_stamp_ms", "tag"):
values[f"imu_{key}"] = numeric_array(window, key, np.int64, -1)
for key in (
"temperature_c", "air_pressure_pa", "accel_x_mps2", "accel_y_mps2", "accel_z_mps2",
"gyro_x_radps", "gyro_y_radps", "gyro_z_radps", "mag_x_ut", "mag_y_ut", "mag_z_ut",
"roll_deg", "pitch_deg", "yaw_deg", "quaternion_w", "quaternion_x", "quaternion_y", "quaternion_z",
):
values[f"imu_{key}"] = numeric_array(window, key, np.float64, np.nan)
values["imu_source_index"] = numeric_array(window, "_source_index", np.int32, -1)
values["imu_source_raw_file_offset"] = numeric_array(window, "source_raw_file_offset", np.int64, -1)
raw_matrix, raw_lengths = raw_frame_matrix(window)
values["imu_raw_frame_bytes"] = raw_matrix
values["imu_raw_frame_length"] = raw_lengths
values["imu_source_files_json_utf8"] = utf8_array(json.dumps([str(path.resolve()) for path in args.imu], ensure_ascii=False))
values["source_lidar_file_utf8"] = utf8_array(source.resolve())
values["segment_name_utf8"] = utf8_array(segment_name)
output = frames_out / f"{segment_name}_{segment_index:06d}.npz"
np.savez_compressed(output, **values)
manifest.append({
"global_index": global_index,
"segment": segment_name,
"segment_index": segment_index,
"output": str(output.relative_to(args.out)),
"source_lidar": str(source.resolve()),
"lidar_time_ns": lidar_time_ns,
"rtk_gga_dt_ns": gga_dt,
"rtk_heading_dt_ns": heading_dt,
"rtk_valid": gga_ok,
"heading_valid": heading_ok,
"rtk_fix_quality": gga_row.get("fix_quality") if gga_ok and gga_row else None,
"rtk_fixed": bool(gga_ok and gga_row and int(gga_row.get("fix_quality", -1)) in {4, 5}),
"imu_window_count": len(window),
})
global_index += 1
fields = sorted({key for row in manifest for key in row})
with (args.out / "manifest.csv").open("w", encoding="utf-8", newline="") as stream:
writer = csv.DictWriter(stream, fieldnames=fields)
writer.writeheader()
writer.writerows(manifest)
summary = {
"frames": len(manifest),
"segments": {name: sum(row["segment"] == name for row in manifest) for name, _ in args.lidar},
"rtk_valid": sum(bool(row["rtk_valid"]) for row in manifest),
"heading_valid": sum(bool(row["heading_valid"]) for row in manifest),
"rtk_fixed": sum(bool(row["rtk_fixed"]) for row in manifest),
"imu_window_nonempty": sum(int(row["imu_window_count"]) > 0 for row in manifest),
"rtk_max_dt_ms": args.rtk_max_dt_ms,
"imu_window_ms": [-args.imu_before_ms, args.imu_after_ms],
"time_basis": "LiDAR and serial host UTC; RTK GNSS time and IMU device time are retained for clock-model refinement",
"imu_orientation_warning": "IMU values are in the raw IMU sensor frame; no LiDAR/body extrinsic is applied",
}
(args.out / "dataset_summary.json").write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(summary, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
LiDAR_RTK_Direct_Calibration/tools/frontlidar_dlog_export.py
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LiDAR_RTK_Direct_Calibration/tools/prepare_multisensor_station_dataset.py
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#!/usr/bin/env python3
"""Prepare one static LiDAR frame and one yaw-only RTK reference pose per NPZ segment."""
from __future__ import annotations
import argparse
import csv
import json
import math
import re
import shutil
from pathlib import Path
from typing import Any
import numpy as np
POSE_FIELDS = ["time", "x", "y", "z", "qx", "qy", "qz", "qw"]
def natural_key(value: str) -> list[Any]:
return [int(part) if part.isdigit() else part.lower() for part in re.split(r"(\d+)", value)]
def truth(value: Any) -> bool:
return str(value).strip().lower() in {"1", "true", "yes", "y"}
def circular_mean_deg(values: np.ndarray) -> float:
radians = np.deg2rad(values)
return float(np.rad2deg(math.atan2(np.mean(np.sin(radians)), np.mean(np.cos(radians)))) % 360.0)
def circular_std_deg(values: np.ndarray) -> float:
radians = np.deg2rad(values)
resultant = max(math.hypot(np.mean(np.cos(radians)), np.mean(np.sin(radians))), 1e-12)
return float(np.rad2deg(math.sqrt(-2.0 * math.log(resultant))))
def geodetic_to_ecef(lat_deg: float, lon_deg: float, height_m: float) -> np.ndarray:
a, e2 = 6378137.0, 6.69437999014e-3
lat, lon = math.radians(lat_deg), math.radians(lon_deg)
sin_lat, cos_lat, sin_lon, cos_lon = math.sin(lat), math.cos(lat), math.sin(lon), math.cos(lon)
n = a / math.sqrt(1.0 - e2 * sin_lat * sin_lat)
return np.array([(n + height_m) * cos_lat * cos_lon, (n + height_m) * cos_lat * sin_lon,
(n * (1.0 - e2) + height_m) * sin_lat], dtype=float)
def ecef_to_enu(ecef: np.ndarray, origin: np.ndarray, lat_deg: float, lon_deg: float) -> np.ndarray:
lat, lon = math.radians(lat_deg), math.radians(lon_deg)
slat, clat, slon, clon = math.sin(lat), math.cos(lat), math.sin(lon), math.cos(lon)
rotation = np.array([[-slon, clon, 0.0], [-slat * clon, -slat * slon, clat],
[clat * clon, clat * slon, slat]], dtype=float)
return rotation @ (ecef - origin)
def yaw_rotation(yaw: float) -> np.ndarray:
c, s = math.cos(yaw), math.sin(yaw)
return np.array([[c, -s, 0.0], [s, c, 0.0], [0.0, 0.0, 1.0]])
def scalar(data: np.lib.npyio.NpzFile, name: str) -> float:
return float(np.asarray(data[name]).reshape(-1)[0])
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--combined-root", type=Path, required=True)
parser.add_argument("--output", type=Path, required=True)
parser.add_argument("--pose-name", default="rtk_gga_raw_heading")
parser.add_argument("--heading-offset-deg", type=float, required=True)
parser.add_argument("--antenna-lever", type=float, nargs=3, required=True, metavar=("X", "Y", "Z"))
parser.add_argument("--accepted-fixes", type=int, nargs="+", default=[4, 5])
parser.add_argument("--heading-std-limit-deg", type=float, default=0.5)
parser.add_argument("--min-stations", type=int, default=30)
parser.add_argument("--expected-stations", type=int, default=0)
parser.add_argument("--overwrite", action="store_true")
return parser.parse_args()
def main() -> int:
args = parse_args()
manifest_path = args.combined_root / "manifest.csv"
with manifest_path.open("r", encoding="utf-8-sig", newline="") as stream:
rows = list(csv.DictReader(stream))
required = {"segment", "output", "lidar_time_ns", "rtk_valid", "heading_valid", "rtk_fix_quality"}
if not rows or not required.issubset(rows[0]):
raise ValueError(f"{manifest_path} is empty or lacks {sorted(required)}")
groups: dict[str, list[dict[str, str]]] = {}
for row in rows:
groups.setdefault(row["segment"], []).append(row)
selected, summaries, rejected = [], [], []
accepted_fixes = set(args.accepted_fixes)
for segment in sorted(groups, key=natural_key):
group = sorted(groups[segment], key=lambda row: int(row["lidar_time_ns"]))
good = [row for row in group if truth(row["rtk_valid"]) and truth(row["heading_valid"])
and int(row["rtk_fix_quality"]) in accepted_fixes]
if not good:
rejected.append({"station": segment, "reason": "no associated fixed RTK position and valid heading"})
continue
samples = []
for row in good:
path = args.combined_root / Path(row["output"])
with np.load(path, allow_pickle=False) as data:
samples.append((scalar(data, "rtk_lat_deg"), scalar(data, "rtk_lon_deg"),
scalar(data, "rtk_altitude_m"), scalar(data, "rtk_raw_heading_deg"),
scalar(data, "rtk_pitch_deg"), scalar(data, "rtk_heading_stddev_deg")))
values = np.asarray(samples, dtype=float)
heading_std = circular_std_deg(values[:, 3])
if heading_std > args.heading_std_limit_deg:
rejected.append({"station": segment, "reason": f"heading std {heading_std:.4f} deg exceeds limit"})
continue
frame = good[len(good) // 2]
source = args.combined_root / Path(frame["output"])
selected.append({"station": segment, "source": source, "time": int(frame["lidar_time_ns"]) / 1e9,
"lat": float(np.mean(values[:, 0])), "lon": float(np.mean(values[:, 1])),
"alt": float(np.mean(values[:, 2])), "heading": circular_mean_deg(values[:, 3])})
summaries.append({"station": segment, "frames": len(group), "valid_fixed_frames": len(good),
"heading_mean_deg": circular_mean_deg(values[:, 3]),
"heading_circular_std_deg": heading_std, "rtk_pitch_mean_deg": float(np.mean(values[:, 4])),
"reported_heading_std_mean_deg": float(np.nanmean(values[:, 5])),
"altitude_std_m": float(np.std(values[:, 2])), "selected_source": str(source)})
if args.expected_stations and len(selected) != args.expected_stations:
raise RuntimeError(f"expected {args.expected_stations} usable stations, got {len(selected)}; rejected={rejected}")
if len(selected) < args.min_stations:
raise RuntimeError(f"need at least {args.min_stations} usable stations, got {len(selected)}; rejected={rejected}")
if args.output.exists() and any(args.output.iterdir()) and not args.overwrite:
raise FileExistsError(f"{args.output} is non-empty; pass --overwrite")
frames = args.output / "frames_all"
frames.mkdir(parents=True, exist_ok=True)
origin = selected[0]
origin_ecef = geodetic_to_ecef(origin["lat"], origin["lon"], origin["alt"])
lever = np.asarray(args.antenna_lever, dtype=float)
pose_rows = []
for index, item in enumerate(selected, 1):
destination = frames / f"station_{index:02d}.npz"
shutil.copy2(item["source"], destination)
antenna = ecef_to_enu(geodetic_to_ecef(item["lat"], item["lon"], item["alt"]), origin_ecef,
origin["lat"], origin["lon"])
corrected_heading = (item["heading"] + args.heading_offset_deg) % 360.0
yaw = math.radians(90.0 - corrected_heading)
reference_position = antenna - yaw_rotation(yaw) @ lever
pose_rows.append(dict(zip(POSE_FIELDS, [item["time"], *reference_position, 0.0, 0.0,
math.sin(yaw / 2.0), math.cos(yaw / 2.0)])))
summaries[index - 1].update({"sequence": index, "prepared_frame": destination.name,
"corrected_heading_deg": corrected_heading})
pose_path = args.output / f"reference_poses_{args.pose_name}.csv"
with pose_path.open("w", encoding="utf-8", newline="") as stream:
writer = csv.DictWriter(stream, fieldnames=POSE_FIELDS); writer.writeheader(); writer.writerows(pose_rows)
with (args.output / "station_summary.csv").open("w", encoding="utf-8", newline="") as stream:
fields = sorted({key for row in summaries for key in row})
writer = csv.DictWriter(stream, fieldnames=fields); writer.writeheader(); writer.writerows(summaries)
document = {"source_combined_root": str(args.combined_root.resolve()), "station_count": len(selected),
"rejected": rejected, "pose_csv": pose_path.name,
"selection_policy": "middle LiDAR frame among fixed-position and valid-heading associations",
"reference_pose_configuration": {"raw_heading_offset_deg": args.heading_offset_deg,
"antenna_lever_body_m": args.antenna_lever,
"orientation_model": "yaw-only, identical to the previous calibration workflow"},
"stations": [{"sequence": i + 1, "source_station": item["station"],
"source_frame": str(item["source"]), "prepared_frame": f"station_{i + 1:02d}.npz"}
for i, item in enumerate(selected)]}
(args.output / "manifest.json").write_text(json.dumps(document, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps({"prepared": str(args.output.resolve()), "stations": len(selected),
"rejected": rejected, "pose_csv": pose_path.name}, ensure_ascii=False, indent=2))
return 0
if __name__ == "__main__":
raise SystemExit(main())
LiDAR_RTK_Direct_Calibration/tools/rscap_v2/audit_capture_v2.py
-22
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from __future__ import annotations
import argparse
from pathlib import Path
from capture_format_v2 import file_summary, read_capture
from pipeline_common import write_json
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("captures", nargs="+", type=Path)
parser.add_argument("--out", type=Path, required=True)
args = parser.parse_args()
summaries = [file_summary(read_capture(path)) for path in args.captures]
write_json(args.out, {"captures": summaries})
for summary in summaries:
print(summary)
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/tools/rscap_v2/capture_format_v2.py
-254
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@@ -1,254 +0,0 @@
from __future__ import annotations
import binascii
import io
import struct
from dataclasses import dataclass, asdict
from pathlib import Path
from typing import BinaryIO, Iterator
FILE_MAGIC = "RAW_SERIAL_CAPTURE_FILE_V2"
RECORD_MAGIC = "RAW_SERIAL_RECORD_V2"
FOOTER_MAGIC = "RAW_SERIAL_CAPTURE_FOOTER_V2"
def read_7bit_int(stream: BinaryIO) -> int:
value = 0
shift = 0
while True:
raw = stream.read(1)
if not raw:
raise EOFError("truncated .NET string length")
value |= (raw[0] & 0x7F) << shift
if not raw[0] & 0x80:
return value
shift += 7
if shift > 35:
raise ValueError("invalid .NET string length")
def read_dotnet_string(stream: BinaryIO) -> str:
length = read_7bit_int(stream)
raw = stream.read(length)
if len(raw) != length:
raise EOFError("truncated .NET string")
return raw.decode("utf-8")
def read_i32(stream: BinaryIO) -> int:
raw = stream.read(4)
if len(raw) != 4:
raise EOFError("truncated int32")
return struct.unpack("<i", raw)[0]
def read_i64(stream: BinaryIO) -> int:
raw = stream.read(8)
if len(raw) != 8:
raise EOFError("truncated int64")
return struct.unpack("<q", raw)[0]
def read_u32(stream: BinaryIO) -> int:
raw = stream.read(4)
if len(raw) != 4:
raise EOFError("truncated uint32")
return struct.unpack("<I", raw)[0]
@dataclass(frozen=True)
class CaptureHeader:
sensor_kind: str
session_id: str
session_start_utc_ticks: int
session_start_monotonic_ticks: int
monotonic_frequency: int
port: str
baud: int
file_start_utc_ticks: int
@dataclass(frozen=True)
class RawChunk:
sequence: int
receive_utc_ticks: int
receive_monotonic_ticks: int
raw: bytes
record_file_offset: int
raw_file_offset: int
record_crc32: int
crc_valid: bool
@dataclass(frozen=True)
class CaptureFooter:
clean_close: bool
records: int
bytes: int
first_sequence: int
last_sequence: int
dropped_chunks: int
dropped_bytes: int
crc_valid: bool
@dataclass
class CaptureFile:
path: str
header: CaptureHeader
chunks: list[RawChunk]
footer: CaptureFooter | None
truncated_tail: bool = False
def read_header(stream: BinaryIO) -> CaptureHeader:
if read_dotnet_string(stream) != FILE_MAGIC:
raise ValueError("not a V2 raw capture file")
version = read_i32(stream)
if version != 2:
raise ValueError(f"unsupported capture version: {version}")
return CaptureHeader(
sensor_kind=read_dotnet_string(stream),
session_id=read_dotnet_string(stream),
session_start_utc_ticks=read_i64(stream),
session_start_monotonic_ticks=read_i64(stream),
monotonic_frequency=read_i64(stream),
port=read_dotnet_string(stream),
baud=read_i32(stream),
file_start_utc_ticks=read_i64(stream),
)
def parse_record_body(body: bytes, record_file_offset: int, record_crc: int) -> RawChunk:
stream = io.BytesIO(body)
if read_dotnet_string(stream) != RECORD_MAGIC:
raise ValueError("invalid record magic")
sequence = read_i64(stream)
receive_utc_ticks = read_i64(stream)
receive_monotonic_ticks = read_i64(stream)
raw_length = read_i32(stream)
if raw_length < 0 or raw_length > 64 * 1024 * 1024:
raise ValueError(f"invalid raw length: {raw_length}")
raw_offset = record_file_offset + 4 + stream.tell()
raw = stream.read(raw_length)
if len(raw) != raw_length:
raise EOFError("truncated raw bytes")
crc_valid = (binascii.crc32(body) & 0xFFFFFFFF) == record_crc
return RawChunk(
sequence=sequence,
receive_utc_ticks=receive_utc_ticks,
receive_monotonic_ticks=receive_monotonic_ticks,
raw=raw,
record_file_offset=record_file_offset,
raw_file_offset=raw_offset,
record_crc32=record_crc,
crc_valid=crc_valid,
)
def parse_footer(body: bytes, expected_crc: int) -> CaptureFooter:
stream = io.BytesIO(body)
if read_dotnet_string(stream) != FOOTER_MAGIC:
raise ValueError("invalid footer magic")
clean_close = stream.read(1) == b"\x01"
records = read_i64(stream)
raw_bytes = read_i64(stream)
first_sequence = read_i64(stream)
last_sequence = read_i64(stream)
dropped_chunks = read_i64(stream)
dropped_bytes = read_i64(stream)
return CaptureFooter(
clean_close=clean_close,
records=records,
bytes=raw_bytes,
first_sequence=first_sequence,
last_sequence=last_sequence,
dropped_chunks=dropped_chunks,
dropped_bytes=dropped_bytes,
crc_valid=(binascii.crc32(body) & 0xFFFFFFFF) == expected_crc,
)
def read_capture(path: Path) -> CaptureFile:
chunks: list[RawChunk] = []
footer = None
truncated = False
with path.open("rb") as stream:
header = read_header(stream)
while True:
record_offset = stream.tell()
length_raw = stream.read(4)
if not length_raw:
break
if len(length_raw) != 4:
truncated = True
break
length = struct.unpack("<i", length_raw)[0]
try:
if length == -1:
footer_length = read_i32(stream)
if footer_length < 0 or footer_length > 1024 * 1024:
raise ValueError("invalid footer length")
footer_body = stream.read(footer_length)
if len(footer_body) != footer_length:
raise EOFError("truncated footer")
footer = parse_footer(footer_body, read_u32(stream))
break
if length <= 0 or length > 64 * 1024 * 1024:
raise ValueError("invalid record length")
body = stream.read(length)
if len(body) != length:
raise EOFError("truncated record body")
record_crc = read_u32(stream)
chunks.append(parse_record_body(body, record_offset, record_crc))
except (EOFError, ValueError):
truncated = True
break
return CaptureFile(str(path), header, chunks, footer, truncated)
def sequence_gaps(chunks: list[RawChunk]) -> list[tuple[int, int, int]]:
result = []
for previous, current in zip(chunks, chunks[1:]):
if current.sequence > previous.sequence + 1:
result.append((previous.sequence, current.sequence, current.sequence - previous.sequence - 1))
return result
def file_summary(capture: CaptureFile) -> dict:
gaps = sequence_gaps(capture.chunks)
sequences = [chunk.sequence for chunk in capture.chunks]
return {
"path": capture.path,
"sensor": capture.header.sensor_kind,
"session_id": capture.header.session_id,
"port": capture.header.port,
"baud": capture.header.baud,
"chunks_read": len(capture.chunks),
"bytes_read": sum(len(chunk.raw) for chunk in capture.chunks),
"first_sequence": sequences[0] if sequences else None,
"last_sequence": sequences[-1] if sequences else None,
"missing_chunks": sum(gap[2] for gap in gaps),
"gap_count": len(gaps),
"bad_record_crc": sum(not chunk.crc_valid for chunk in capture.chunks),
"truncated_tail": capture.truncated_tail,
"footer": None if capture.footer is None else asdict(capture.footer),
"gaps": gaps[:100],
}
def iter_contiguous_segments(chunks: list[RawChunk]) -> Iterator[tuple[int, list[RawChunk]]]:
if not chunks:
return
segment_id = 0
current = [chunks[0]]
for previous, chunk in zip(chunks, chunks[1:]):
if chunk.sequence != previous.sequence + 1:
yield segment_id, current
segment_id += 1
current = [chunk]
else:
current.append(chunk)
yield segment_id, current
LiDAR_RTK_Direct_Calibration/tools/rscap_v2/parse_rtk_imu_v2.py
-35
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@@ -1,35 +0,0 @@
from __future__ import annotations
import argparse
from pathlib import Path
from capture_format_v2 import file_summary, read_capture
from pipeline_common_corrected import parse_imu_capture, parse_rtk_capture, write_json, write_jsonl
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--rtk", type=Path, required=True)
parser.add_argument("--imu", type=Path, required=True)
parser.add_argument("--out", type=Path, required=True)
args = parser.parse_args()
args.out.mkdir(parents=True, exist_ok=True)
rtk_capture = read_capture(args.rtk)
imu_capture = read_capture(args.imu)
rtk_rows = parse_rtk_capture(rtk_capture)
imu_rows = parse_imu_capture(imu_capture)
write_jsonl(args.out / "rtk.jsonl", rtk_rows)
write_jsonl(args.out / "imu.jsonl", imu_rows)
write_json(args.out / "parse_summary.json", {
"rtk_capture": file_summary(rtk_capture),
"imu_capture": file_summary(imu_capture),
"rtk_records": len(rtk_rows),
"rtk_checksum_valid": sum(bool(row.get("checksum_valid")) for row in rtk_rows),
"imu_frames": len(imu_rows),
"imu_crc_valid": sum(bool(row.get("crc_valid")) for row in imu_rows),
})
print(f"RTK records={len(rtk_rows)}, IMU frames={len(imu_rows)}")
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/tools/rscap_v2/pipeline_common.py
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@@ -1,300 +0,0 @@
from __future__ import annotations
import binascii
import json
import math
import struct
from pathlib import Path
from typing import Iterable
from capture_format_v2 import CaptureFile, RawChunk, iter_contiguous_segments, read_capture
DOTNET_UNIX_EPOCH_TICKS = 621355968000000000
def ticks_to_unix_ns(ticks: int) -> int:
return (ticks - DOTNET_UNIX_EPOCH_TICKS) * 100
def safe_float(value: str, default=None):
try:
return float(value)
except (TypeError, ValueError):
return default
def safe_int(value: str, default=None):
try:
return int(value)
except (TypeError, ValueError):
return default
def nmea_checksum_valid(line: str) -> bool:
star = line.rfind("*")
if star < 0:
return False
try:
expected = int(line[star + 1:star + 3], 16)
except ValueError:
return False
value = 0
for char in line[1:star]:
value ^= ord(char)
return value == expected
def unicore_crc32(text: str) -> int:
crc = 0
for value in text.encode("ascii", "replace"):
crc ^= value
for _ in range(8):
crc = (crc >> 1) ^ (0xEDB88320 if crc & 1 else 0)
return crc & 0xFFFFFFFF
def unicore_checksum_valid(line: str) -> bool:
star = line.rfind("*")
if star < 0 or len(line) < star + 9:
return False
try:
expected = int(line[star + 1:star + 9], 16)
except ValueError:
return False
return unicore_crc32(line[1:star]) == expected
def parse_checksum(line: str) -> bool:
if line.startswith("$"):
return nmea_checksum_valid(line)
if line.startswith("#"):
return unicore_checksum_valid(line)
return False
def parse_nmea_latlon(value: str, hemisphere: str):
raw = safe_float(value)
if raw is None:
return None
degrees = math.floor(raw / 100.0)
result = degrees + (raw - degrees * 100.0) / 60.0
if hemisphere.upper() in ("S", "W"):
result = -result
return result
def parse_gga(line: str) -> dict:
fields = line[:line.rfind("*")].split(",")
if len(fields) < 10:
raise ValueError("GGA has too few fields")
return {
"type": "GGA",
"position_time_utc": fields[1],
"lat_deg": parse_nmea_latlon(fields[2], fields[3]),
"lon_deg": parse_nmea_latlon(fields[4], fields[5]),
"fix_quality": safe_int(fields[6], -1),
"satellites": safe_int(fields[7], -1),
"hdop": safe_float(fields[8]),
"altitude_m": safe_float(fields[9]),
"geoid_separation_m": safe_float(fields[11]) if len(fields) > 11 else None,
"differential_age_s": safe_float(fields[13]) if len(fields) > 13 else None,
"station_id": fields[14].strip('"') if len(fields) > 14 else "",
}
def parse_heading(line: str) -> dict:
before_crc = line[:line.rfind("*")]
header, payload = before_crc.split(";", 1)
header_fields = header.split(",")
fields = payload.split(",")
if len(fields) < 7:
raise ValueError("UNIHEADINGA has too few fields")
raw_heading = safe_float(fields[3])
return {
"type": "UNIHEADINGA",
"gnss_week": safe_int(header_fields[4]) if len(header_fields) > 4 else None,
"gnss_tow_ms": safe_int(header_fields[5]) if len(header_fields) > 5 else None,
"heading_status": fields[0],
"heading_solution": fields[1],
"baseline_length_m": safe_float(fields[2]),
"raw_heading_deg": raw_heading,
"pitch_deg": safe_float(fields[4]),
"heading_stddev_deg": safe_float(fields[6]),
"pitch_stddev_deg": safe_float(fields[7]) if len(fields) > 7 else None,
"station_id": fields[8].strip('"') if len(fields) > 8 else "",
"satellites": safe_int(fields[9], -1) if len(fields) > 9 else -1,
"solution_satellites": safe_int(fields[10], -1) if len(fields) > 10 else -1,
"observations": safe_int(fields[11], -1) if len(fields) > 11 else -1,
"multi_count": safe_int(fields[12], -1) if len(fields) > 12 else -1,
"heading_valid": fields[0] == "SOL_COMPUTED" and fields[1] in {"NARROW_INT", "NARROW_FLOAT"},
}
def chunk_source(chunks: list[RawChunk], offset: int, end: int) -> dict:
first = chunks[0]
last = chunks[-1]
cursor = 0
start_chunk = first
end_chunk = last
for chunk in chunks:
chunk_start = cursor
chunk_end = cursor + len(chunk.raw)
if chunk_start <= offset < chunk_end:
start_chunk = chunk
if chunk_start < end <= chunk_end:
end_chunk = chunk
break
cursor = chunk_end
return {
"source_segment_id": None,
"source_chunk_sequence_first": start_chunk.sequence,
"source_chunk_sequence_last": end_chunk.sequence,
"source_raw_file_offset": start_chunk.raw_file_offset + max(0, offset - sum(len(c.raw) for c in chunks if c.sequence < start_chunk.sequence)),
"source_raw_byte_length": max(0, end - offset),
}
def parse_rtk_capture(capture: CaptureFile) -> list[dict]:
rows = []
for segment_id, chunks in iter_contiguous_segments(capture.chunks):
stream = b"".join(chunk.raw for chunk in chunks)
cursor = 0
while cursor < len(stream):
newline = stream.find(b"\n", cursor)
if newline < 0:
break
end = newline + 1
raw_line = stream[cursor:end].rstrip(b"\r\n")
cursor = end
if not raw_line:
continue
line = raw_line.decode("ascii", "replace")
valid = parse_checksum(line)
row = {
"type": "UNKNOWN",
"raw_line": line,
"checksum_valid": valid,
"host_receive_utc_ns": ticks_to_unix_ns(chunks[-1].receive_utc_ticks),
"host_receive_monotonic_ticks": chunks[-1].receive_monotonic_ticks,
"source_segment_id": segment_id,
"source_byte_offset_in_segment": cursor - len(raw_line) - 1,
"source_byte_length": len(raw_line) + 1,
}
try:
if line.startswith("$GNGGA") or line.startswith("$GPGGA"):
row.update(parse_gga(line))
elif line.startswith("#UNIHEADINGA"):
row.update(parse_heading(line))
except ValueError as ex:
row["parse_error"] = str(ex)
rows.append(row)
return rows
def crc16_hi13(data: bytes) -> int:
crc = 0
for value in data:
crc ^= value << 8
for _ in range(8):
crc = ((crc << 1) ^ 0x1021) & 0xFFFF if crc & 0x8000 else (crc << 1) & 0xFFFF
return crc
def decode_hi91(frame: bytes) -> dict:
f32 = lambda i: struct.unpack_from("<f", frame, i)[0]
return {
"tag": 0x91,
"pps_sync_stamp_ms": int.from_bytes(frame[7:9], "little"),
"temperature_c": struct.unpack_from("<b", frame, 9)[0],
"air_pressure_pa": f32(10),
"device_timestamp_ms": int.from_bytes(frame[14:18], "little"),
"accel_x_mps2": f32(18) * 9.80665,
"accel_y_mps2": f32(22) * 9.80665,
"accel_z_mps2": f32(26) * 9.80665,
"gyro_x_radps": f32(30) * math.pi / 180.0,
"gyro_y_radps": f32(34) * math.pi / 180.0,
"gyro_z_radps": f32(38) * math.pi / 180.0,
"mag_x_ut": f32(42), "mag_y_ut": f32(46), "mag_z_ut": f32(50),
"roll_deg": f32(54), "pitch_deg": f32(58), "yaw_deg": f32(62),
"quaternion_w": f32(66), "quaternion_x": f32(70),
"quaternion_y": f32(74), "quaternion_z": f32(78),
}
def decode_hi92(frame: bytes) -> dict:
i16 = lambda i: struct.unpack_from("<h", frame, i)[0]
i32 = lambda i: struct.unpack_from("<i", frame, i)[0]
return {
"tag": 0x92,
"status": int.from_bytes(frame[7:9], "little"),
"temperature_c": struct.unpack_from("<b", frame, 9)[0],
"pps_sync_stamp_ms": int.from_bytes(frame[10:12], "little"),
"air_pressure_pa": i16(12) + 100000.0,
"heave_m": i16(14) * 0.001,
"gyro_x_radps": i16(16) * 0.001, "gyro_y_radps": i16(18) * 0.001, "gyro_z_radps": i16(20) * 0.001,
"accel_x_mps2": i16(22) * 0.0048828, "accel_y_mps2": i16(24) * 0.0048828, "accel_z_mps2": i16(26) * 0.0048828,
"mag_x_ut": i16(28) * 0.030517, "mag_y_ut": i16(30) * 0.030517, "mag_z_ut": i16(32) * 0.030517,
"roll_deg": i32(34) * 0.001, "pitch_deg": i32(38) * 0.001, "yaw_deg": i32(42) * 0.001,
"quaternion_w": i16(46) * 0.0001, "quaternion_x": i16(48) * 0.0001,
"quaternion_y": i16(50) * 0.0001, "quaternion_z": i16(52) * 0.0001,
}
def parse_imu_capture(capture: CaptureFile) -> list[dict]:
rows = []
for segment_id, chunks in iter_contiguous_segments(capture.chunks):
stream = b"".join(chunk.raw for chunk in chunks)
cursor = 0
while True:
start = stream.find(b"\x5a\xa5", cursor)
if start < 0 or start + 6 > len(stream):
break
payload_length = int.from_bytes(stream[start + 2:start + 4], "little")
frame_length = 6 + payload_length
if payload_length <= 0 or payload_length > 512:
cursor = start + 1
continue
if start + frame_length > len(stream):
break
frame = stream[start:start + frame_length]
expected = int.from_bytes(frame[4:6], "little")
actual = crc16_hi13(frame[:4] + frame[6:])
end = start + frame_length
source = chunk_source(chunks, start, end)
source["source_segment_id"] = segment_id
row = {
"type": "HI13",
"tag": frame[6],
"frame_length": frame_length,
"crc_valid": expected == actual,
"host_receive_utc_ns": ticks_to_unix_ns(chunks[-1].receive_utc_ticks),
"host_receive_monotonic_ticks": chunks[-1].receive_monotonic_ticks,
"source_segment_id": segment_id,
"source_byte_offset_in_segment": start,
"source_byte_length": frame_length,
"raw_frame_hex": frame.hex(),
}
if expected == actual:
try:
row.update(decode_hi91(frame) if frame[6] == 0x91 else decode_hi92(frame) if frame[6] == 0x92 else {})
except (IndexError, struct.error, ValueError) as ex:
row["parse_error"] = str(ex)
rows.append(row)
cursor = end
return rows
def write_jsonl(path: Path, rows: Iterable[dict]) -> None:
with path.open("w", encoding="utf-8", newline="\n") as stream:
for row in rows:
stream.write(json.dumps(row, ensure_ascii=False, separators=(",", ":")) + "\n")
def write_json(path: Path, value: dict) -> None:
path.write_text(json.dumps(value, ensure_ascii=False, indent=2), encoding="utf-8")
def load_jsonl(path: Path) -> list[dict]:
with path.open("r", encoding="utf-8") as stream:
return [json.loads(line) for line in stream if line.strip()]
LiDAR_RTK_Direct_Calibration/tools/rscap_v2/pipeline_common_corrected.py
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from __future__ import annotations
import bisect
from pipeline_common import *
from capture_format_v2 import CaptureFile, RawChunk, iter_contiguous_segments
_SPAN_CACHE: dict[int, tuple[list[RawChunk], list[int]]] = {}
def _chunk_starts(chunks: list[RawChunk]) -> list[int]:
key = id(chunks)
cached = _SPAN_CACHE.get(key)
if cached is not None and cached[0] is chunks:
return cached[1]
starts = []
cursor = 0
for chunk in chunks:
starts.append(cursor)
cursor += len(chunk.raw)
_SPAN_CACHE[key] = (chunks, starts)
return starts
def source_for_span(chunks: list[RawChunk], start: int, end: int, segment_id: int) -> dict:
starts = _chunk_starts(chunks)
start_index = max(0, min(len(chunks) - 1, bisect.bisect_right(starts, start) - 1))
end_index = max(start_index, min(len(chunks) - 1, bisect.bisect_left(starts, end) - 1))
start_chunk = chunks[start_index]
end_chunk = chunks[end_index]
return {
"source_segment_id": segment_id,
"source_chunk_sequence_first": start_chunk.sequence,
"source_chunk_sequence_last": end_chunk.sequence,
"source_raw_file_offset": start_chunk.raw_file_offset + (start - starts[start_index]),
"source_raw_byte_length": end - start,
"host_receive_utc_ns": ticks_to_unix_ns(end_chunk.receive_utc_ticks),
"host_receive_monotonic_ticks": end_chunk.receive_monotonic_ticks,
}
def parse_rtk_capture(capture: CaptureFile) -> list[dict]:
rows = []
for segment_id, chunks in iter_contiguous_segments(capture.chunks):
stream = b"".join(chunk.raw for chunk in chunks)
cursor = 0
while cursor < len(stream):
newline = stream.find(b"\n", cursor)
if newline < 0:
break
end = newline + 1
raw_line = stream[cursor:end].rstrip(b"\r\n")
start = cursor
cursor = end
if not raw_line:
continue
line = raw_line.decode("ascii", "replace")
row = {"type": "UNKNOWN", "raw_line": line, "checksum_valid": parse_checksum(line)}
row.update(source_for_span(chunks, start, end, segment_id))
try:
if line.startswith("$GNGGA") or line.startswith("$GPGGA"):
row.update(parse_gga(line))
elif line.startswith("#UNIHEADINGA"):
row.update(parse_heading(line))
except ValueError as ex:
row["parse_error"] = str(ex)
rows.append(row)
return rows
def parse_imu_capture(capture: CaptureFile) -> list[dict]:
rows = []
for segment_id, chunks in iter_contiguous_segments(capture.chunks):
stream = b"".join(chunk.raw for chunk in chunks)
cursor = 0
while True:
start = stream.find(b"\x5a\xa5", cursor)
if start < 0 or start + 6 > len(stream):
break
payload_length = int.from_bytes(stream[start + 2:start + 4], "little")
frame_length = 6 + payload_length
if payload_length <= 0 or payload_length > 512:
cursor = start + 1
continue
if start + frame_length > len(stream):
break
frame = stream[start:start + frame_length]
expected = int.from_bytes(frame[4:6], "little")
actual = crc16_hi13(frame[:4] + frame[6:])
end = start + frame_length
row = {
"type": "HI13",
"tag": frame[6],
"frame_length": frame_length,
"crc_valid": expected == actual,
"raw_frame_hex": frame.hex(),
}
row.update(source_for_span(chunks, start, end, segment_id))
if row["crc_valid"]:
try:
row.update(decode_hi91(frame) if frame[6] == 0x91 else decode_hi92(frame) if frame[6] == 0x92 else {})
except (IndexError, struct.error, ValueError) as ex:
row["parse_error"] = str(ex)
rows.append(row)
cursor = end
return rows
PAIR_DIAGNOSTICS.md
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# Pair 局部诊断与全局灵敏度扫描
本说明补充主 README。所有变换均采用：
```text
X = T_body_lidar
A_ij X = X B_ij
B_ij = T_Li_Lj（把站点 j 点云变到站点 i）
```
模式 4 相对模式 3 的数值差定义为：
```text
Delta_ij = B_ij^-1 * (X^-1 * A_ij * X)
```
打印的平移 xyz 和旋转 RPY 是 `Delta_ij` 在站点 j 雷达局部坐标系中的分量，不是屏幕坐标。3D 相机视角会改变画面中的“横向”，所以不能仅凭屏幕左右判断车体系 Y 或 yaw。
## Pair 0 当前诊断
Open3D 精筛 Pair 0（station 0 <- 1）的当前结果为：
```text
translation xyz = [-1.2535, +1.4157, +7.7441] cm
rotation RPY xyz = [-0.5355, -0.1093, +0.0247] deg
norm = 7.9716 cm / 0.547109 deg
```
因此这对主要表现为 roll/pitch 相关姿态差和相对 Z 差，yaw 仅约 `0.025 deg`，不应优先调 yaw。
## 可视化试验
查看原结果：
```powershell
$Repo = "D:\你的代码目录\calibration"
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_open3d_result.ps1" -PairIndex 0
```
试验车体系左乘 pitch `+0.2 deg`：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_open3d_result.ps1" -PairIndex 0 -LeftPitchDeg 0.2
```
此时按键含义：`3` 为 GICP 的 B，`4` 为当前最终 X 预测，`5` 为试验修正后的 X 预测。试验使用：
```text
X_test = DeltaR_body * X
```
这是真正的车体系左乘，会同时旋转 X 的旋转部分和平移向量；小角度下才近似等价于直接给 JSON 的 pitch 加相同角度。
## 全部运动对扫描
运行：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_sensitivity_scan.ps1" -PairIndex 0
```
程序扫描：
- pitch：`+0.1/+0.2/+0.3 deg`；
- 在这些 pitch 及零 pitch 附近扫描 roll：`-0.2/-0.1/+0.1 deg`；
- yaw `-0.2/+0.2 deg` 只作为低灵敏度对照；
- Open3D 全部精筛对、small_gicp 全部精筛对、跨后端共识对分别计算；
- 同时报告地面法向与高度残差；
- 使用 `0.05 m / 0.5 deg` 作为透明的归一化尺度，统计全局 RMS、改善对数和恶化对数。
输出位于 `results/diagnostics/`。JSON 保存逐对结果，CSV 便于横向比较。
## 接受规则
局部扫描只用于定位，程序不会覆盖 `final_extrinsic_recommended.json`。候选至少需要满足：
1. 不能只改善 Pair 0；全部精筛对的归一化 RMS 应下降；
2. 改善的运动对数量应多于恶化数量；
3. Open3D、small_gicp 与共识集合应给出相同方向的趋势；
4. 地面法向和高度约束不能明显恶化；
5. 多个代表性运动对的模式 4/5 可视化应同步改善。
如果只有 Pair 0 改善，应把它视为局部配准或场景问题，不修改全局 X。
对于当前仅含 yaw 的 RTK A，车体系 Z 轴平移在 AX=XB 中不可观。扫描程序会用 `X.z + 0.10 m` 做数值检查，但不会扫描或修改 z；z 必须由地面高度约束或外部量测确定。
README.md
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# LiDAR–双天线 RTK 手眼标定
# 双天线RTK—3D LiDAR直接手眼标定
本仓库提供一套可从原始 Medulla 记录复现的静态站点标定流程，求解三维激光雷达到后轮轴中心车体系的外参
本仓库从静态站点原始数据复现 `T_RTK_lidar`：把原始雷达坐标转换到RTK导航坐标系。它**不是** `base_link` 车体外参，也不会在求解阶段使用车体航向偏置或RTK到后轮轴的XY杆臂。
## 1. 输出坐标约定
统一约定 `T_A_B` 把B系点变换到A系：
```text
X = T_body_lidar
p_RTK = T_RTK_lidar · p_lidar
```
约定 `T_A_B` 将 B 系坐标变换到 A 系。对任意站点 i、j：
RTK导航系在本仓库中定义为：
- 原点：GGA位置参考点（通常为ANT1相位中心，必须结合接收机配置确认）；
- X轴：`rawHeading`所表示的双天线基线在水平面的投影；
- Y轴：左；
- Z轴：上；
- ENU航向：`yaw = 90° - rawHeading`；
- roll、pitch：当前轨迹中固定为0。
如果下游需要 `T_body_lidar`，必须另有经过确认的 `T_body_rtk`：
```text
A_ij = T_W_Bi^-1 T_W_Bj # RTK 给出的车体相对运动
B_ij = T_Li_Lj # GICP 给出的雷达相对运动
A_ij X = X B_ij
T_body_lidar = T_body_rtk · T_RTK_lidar
```
当前部署建议仍采用 [results/01_previous_two_batches/final_extrinsic_deployment.json](results/01_previous_two_batches/final_extrinsic_deployment.json)。data4 是一次独立重算，结果与部署值相差约 `1.592 cm / 0.234°`，但自身 AX 残差更高，因此只作为候选和稳定性证据，不自动替换部署值。
## 2. 算法流程
## 标定总流程
```mermaid
flowchart LR
raw["原始站点 dlog / RTK、IMU rscap"] --> export["分别解析并统一到时间轴"]
export --> assoc["按每个 LiDAR 帧关联 RTK/IMU，导出 NPZ"]
assoc --> prep["每站选一帧，构建 RTK 车体位姿 A"]
prep --> b1["small_gicp 求 B"]
prep --> b2["Open3D GICP 求 B"]
b1 --> gate["与 X 无关的质量筛选及双后端一致性"]
b2 --> gate
gate --> solve["AX=XB + 地面约束求 X"]
solve --> check["残差、bootstrap、条件数、跨批复核和 3D 可视化"]
```text
逐站LiDAR dlog + RTK.rscap + IMU.rscap
→ 分别解析并保留原始字段
→ 以LiDAR帧时间为索引关联RTK/IMU，生成combined NPZ
→ 每站选择一帧静态点云，GGA转局部ENU，rawHeading构造yaw-only RTK pose
→ Open3D GICP和small_gicp分别求 B_ij = T_Li_Lj
→ 留出点、Hessian、正反向、多初值和旋转共轭不变量筛选
→ 两后端共同认可的边形成consensus B
→ A_ij X = X B_ij + 地面法向/高度约束求 X = T_RTK_lidar
→ bootstrap、双后端差异、逐对残差和3D可视化检查
```
流程有两个原始数据入口：
代码实际使用：
- 旧式数据：LiDAR 和 `GPS-POST-Z` 位于每个站点 dlog 中，使用 `export_legacy_stations.ps1`。
- 新式多传感器数据：LiDAR 位于逐站 dlog，RTK 与 IMU 是独立 `.rscap`，使用 `export_multisensor_stations.ps1`。处理顺序是统一时间轴、分别解析、按 LiDAR 帧关联、导出 NPZ。
```text
A_ij = inv(T_W_Ri) · T_W_Rj = T_Ri_Rj
B_ij = T_Li_Lj # 将站点j点云变换到站点i
A_ij · X = X · B_ij
X = T_RTK_lidar
```
IMU 会在新式数据中原样解析并随 LiDAR 帧关联保存，但当前 LiDAR–RTK 外参求解不使用 IMU，也不做运动畸变校正，因为每一站采集点云时车辆静止。IMU 外参应使用单独的激励数据和专用标定流程求解。
## 3. 原始数据目录
## 三批数据的角色
大体积数据不提交Git。`DataRoot`下每个站点必须是一个独立dlog目录，至少包含：
| 数据 | 原始格式 | 站点 | RTK 情况 | 在本仓库中的角色 |
|---|---|---:|---|---|
| 第一批 | 逐站 dlog，内嵌 GPS-POST-Z | 38 | 约 10 秒一条，部分站仅 1–11 个有效样本 | 辅助复核，不承担主要求解 |
| 第二批 | 逐站 dlog，内嵌 GPS-POST-Z | 38 | 每站约 125–412 个有效样本，航向稳定 | 现部署外参的主要求解数据 |
| data4 | 逐站 LiDAR dlog + 独立 RTK/IMU rscap | 34 | 11,678 个 LiDAR 帧均成功关联 fixed RTK、heading 和 IMU | 独立重算与跨批比较 |
```text
raw_dataset/
├── stations/
│ ├── 001/
│ │ ├── dobject/
│ │ └── dobject_recording/
│ ├── 002/
│ └── ...
└── captures/
├── rtk.rscap
└── imu.rscap
```
原始数据体积较大，不在 Git 仓库中。复现者应从云盘取得第一批、第二批或 data4 的原始目录，并在命令行传入路径。
每个站点应在车辆完全静止后记录点云；建议不少于30站，并包含充足的直行、左转、右转和大角度转向姿态变化。
## 环境
## 4. 环境安装
- Windows PowerShell 5.1 或 PowerShell 7
- Python 3.10+
- `pip install -r requirements.txt`
- `small_gicp` 后端需要可导入 `small_gicp`；Open3D 后端需要 `open3d`
所有脚本从自身位置推导仓库根目录。数据和输出路径均由参数传入，不依赖开发者电脑上的固定路径。
## 从原始数据开始复现
以下路径只表示格式，请替换为自己的目录。
### A. 第一批、第二批旧式 dlog
已验证环境为Windows、PowerShell、Python 3.11。安装依赖：
```powershell
$Repo = "D:\你的代码目录\calibration"
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\export_legacy_stations.ps1" `
-DataRoot "D:\你的数据目录\batch2_raw" `
-OutputRoot "D:\你的输出目录\batch2_export"
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\prepare_legacy_dataset.ps1" `
-ExportRoot "D:\你的输出目录\batch2_export" `
-Output "D:\你的输出目录\batch2_prepared" `
-HeadingOffsetDeg 21.226 `
-AntennaLever -0.320,-0.365,0.620 `
-ExpectedStations 38 `
-HeadingStdLimitDeg 0.5
python -m pip install -r requirements.txt
```
第一批采用同一导出方式，但应在导出命令显式添加 `-RtkMaxDtMs 15000`；因其 RTK 稀疏，准备阶段也不建议沿用 `0.5°` 的严格站内航向离散度阈值。上述 `21.226°` 和杆臂 `[-0.320,-0.365,0.620] m` 是本项目已有两批数据采用的配置，不是通用常数；换车或改变天线安装后必须重新确认。
依赖包括NumPy、SciPy、Open3D和small_gicp。若small_gicp没有对应Windows wheel，可在WSL2中安装后运行Python核心命令，或先只运行Open3D后端；完整共识流程需要两个后端都可用。
### B. data4 式独立 RTK/IMU rscap
## 5. 从原始数据一键复现
在仓库根目录执行，路径由使用者通过参数传入，脚本内没有本机绝对路径：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\export_multisensor_stations.ps1" `
-DataRoot "D:\你的数据目录\data4_raw" `
-RtkCapture "D:\你的数据目录\captures\rtk.rscap" `
-ImuCapture "D:\你的数据目录\captures\imu.rscap" `
-OutputRoot "D:\你的输出目录\data4_export"
$Repo = (Resolve-Path ".").Path
$Raw = "E:\calibration_data\data4"
$Out = "E:\calibration_output\rtk_lidar"
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\prepare_multisensor_dataset.ps1" `
-CombinedRoot "D:\你的输出目录\data4_export\combined" `
-Output "D:\你的输出目录\data4_prepared" `
-HeadingOffsetDeg 21.226 `
-AntennaLever -0.320,-0.365,0.620 `
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_full_pipeline.ps1" `
-DataRoot "$Raw\stations" `
-RtkCapture "$Raw\captures\rtk.rscap" `
-ImuCapture "$Raw\captures\imu.rscap" `
-OutputRoot $Out `
-RtkReferenceHeightAboveGroundM 0.8535 `
-ExpectedStations 34
```
### C. 运行标定
单批数据同时运行 small_gicp、Open3D GICP 和跨后端共识：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_single_dataset.ps1" `
-Prepared "D:\你的输出目录\data4_prepared" `
-OutputRoot "D:\你的输出目录\data4_calibration" `
-BodyHeight 0.2335
```
复现本仓库“第二批求解、第一批辅助复核”的历史流程：
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_all.ps1" `
-Batch1Prepared "D:\你的输出目录\batch1_prepared" `
-Batch2Prepared "D:\你的输出目录\batch2_prepared" `
-OutputRoot "D:\你的输出目录\two_batch_calibration"
```
`BodyHeight=0.2335 m` 是后轮轴中心离地高度，用于把雷达地面平面约束转换到车体原点；它不是雷达离地高度。换轮胎、胎压或车体载荷后应重新测量并评估不确定度。
## 如何判断结果
不能仅凭 `AX=XB` 残差或一张叠图宣称外参正确。至少同时检查：
1. B 的配准质量：收敛、有效对应点数/比例、RMSE、Hessian 信息矩阵特征值与条件数、small_gicp 与 Open3D 的相对运动一致性。
2. X 的可观性：加权雅可比条件数越接近 1 越均衡；极大值说明存在弱方向，但没有脱离尺度和数据分布的单一硬阈值。本次 data4 为 `7.44`，不是病态。
3. AX 残差分布：看 RMS、median、P90/P95、max 和逐对异常，而不是只看均值。
4. 重采样稳定性：bootstrap 的六自由度标准差及置信区间。
5. 跨批检查：同一 X 在独立批次的残差，以及两批独立求出的 X 之 SE(3) 差。
6. 3D 可视化：模式 3 的 B 与模式 4 的 `X^-1 A X` 是否都使相同墙面、杆件和地面重合。
当前没有把“增量小于某个值”当成绝对真值判据。工程筛查可先关注多数优质运动对是否约在厘米级到数厘米、亚度级；但阈值必须结合场景尺度、点云分辨率、RTK 质量和车辆运动幅度制定。模式 3 已错位时优先检查 B；模式 3 正常而模式 4 系统性错位时，再检查 A、坐标约定和 X。
## 结果摘要
| 结果 | 平移 xyz (m) | roll/pitch/yaw (deg) | AX 平移/旋转 RMS | 结论 |
|---|---|---|---|---|
| 部署值（第二批求解） | `[1.297760,-0.000067,0.720498]` | `[-0.785151,1.202661,-0.835510]` | `0.07985 m / 0.96118°`，39 对 | 当前建议部署 |
| data4 独立候选 | `[1.300376,-0.001707,0.704877]` | `[-0.791892,1.393823,-0.970743]` | `0.11762 m / 1.24257°`，26 对 | 独立候选，不替换部署值 |
data4 候选相对部署值变化 `1.592 cm / 0.234°`，其中 z 低 `1.562 cm`。旧部署值作用于 data4 的残差约 `0.11953 m / 1.24836°`；data4 候选作用于历史第二批的残差约 `0.07931 m / 0.98926°`。两个 X 的跨批表现接近，当前数据不足以证明 data4 的较低 z 更接近真值。
## 仓库目录
主要输出：
```text
code/ 标定、配准筛选、共识、比较和可视化核心程序
tools/ 原始 dlog/rscap 解析、时间关联、NPZ 导出和数据准备
run/ 不含本机固定路径的 PowerShell 入口
results/ 历史两批、data4 与跨批比较三个结果目录
$Out/
├── exported/
│ ├── export/ # 各站LiDAR逐帧NPZ
│ ├── parsed/ # RTK/IMU JSONL
│ └── combined/ # 按LiDAR帧关联后的多传感器NPZ
├── prepared_rtk_direct/
│ ├── frames_all/ # 每站选中的静态帧
│ └── reference_poses_rtk_gga_raw_heading.csv
└── calibration/
├── open3d_gicp/
├── small_gicp/
├── consensus/
├── summary.json
└── final_T_RTK_lidar.json
```
完整复现流程和所有主要文件职责均在本 README；`run/README.md`、`tools/README.md` 和 [results/README.md](results/README.md) 只是目录内快速索引。
若已经有`combined/`，可跳过原始导出：
## 代码、工具和运行入口职责
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\run_direct_rtk_lidar.ps1" `
-CombinedRoot "E:\calibration_output\exported\combined" `
-WorkRoot "E:\calibration_output\prepared_rtk_direct" `
-OutputRoot "E:\calibration_output\calibration" `
-RtkReferenceHeightAboveGroundM 0.8535 `
-ExpectedStations 34
```
根 README 是本仓库唯一的完整复现说明。`run/README.md`、`tools/README.md` 和 `results/README.md` 只作为进入对应目录时的快速索引，不承载另一套流程。
## 6. 3D可视化
### code：标定核心
```powershell
powershell.exe -NoProfile -ExecutionPolicy Bypass -File "$Repo\run\view_result.ps1" `
-Frames "$Out\prepared_rtk_direct\frames_all" `
-Pairs "$Out\calibration\consensus\B_consensus.npz" `
-Extrinsic "$Out\calibration\final_T_RTK_lidar.json" `
-PairIndex 0
```
| 文件 | 职责 |
|---|---|
| `rigorous_calibration.py` | 核心 CLI。`ground` 拟合每站地面；`pairs` 用 small_gicp 或 Open3D GICP 求 B 和质量指标；`calibrate` 联合 AX=XB 与地面约束求 X；`validate` 计算指定 X 的逐对残差。 |
| `refine_pairs.py` | 根据收敛、RMSE、对应关系、Hessian/信息矩阵和运动覆盖筛选 B；不读取 X，避免循环挑选。 |
| `cross_backend_filter.py` | 对齐 Open3D 与 small_gicp 的同一站点对，只保留两个后端相互一致的 B。 |
| `finalize_consensus.py` | 汇总历史两批的 consensus B、外参和第一批辅助复核。 |
| `summarize_results.py` | 汇总两个后端的外参、B 质量和跨批检查，生成推荐结果。 |
| `compare_extrinsics.py` | 在 SE(3) 上计算两套外参的严格相对平移和旋转差。 |
| `visualize_pair_3d.py` | 交互显示原始点云、RTK A、GICP B、`X^-1AX`，并打印 `B^-1(X^-1AX)` 数值增量。 |
| `scan_extrinsic_sensitivity.py` | 对 X 左乘小角度 roll/pitch/yaw 扰动，检查指定运动对的局部敏感方向。 |
窗口中：
### tools：原始数据到 prepared
- 蓝色：目标站点i；橙色：站点j；
- `1`：原始点云；
- `2`：RTK运动A直接作为初值；
- `3`：GICP测得的B；
- `4`：最终外参预测的 `X^-1 A X`；
- `Q/Esc`：退出。
| 文件 | 职责 |
|---|---|
| `frontlidar_dlog_export.py` | 从 Medulla dlog 导出传感器坐标系 `points_raw`；旧格式可同时匹配站内 GPS-POST-Z。 |
| `prepare_station_dataset.py` | 从旧式逐站导出中每站选择一帧，计算 yaw-only RTK 后轮轴位姿并生成 prepared。 |
| `build_multisensor_npz.py` | 将独立 LiDAR、RTK、IMU 统一到 LiDAR 帧索引并生成 combined NPZ。 |
| `prepare_multisensor_station_dataset.py` | 从 combined NPZ 选择每站静止帧，生成与旧流程相同的 prepared 接口。 |
| `rscap_v2/capture_format_v2.py` | 读取 rscap v2 文件头、原始记录块和文件尾。 |
| `rscap_v2/audit_capture_v2.py` | 审计 capture 完整性、时间范围和记录统计。 |
| `rscap_v2/parse_rtk_imu_v2.py` | 分别解析 RTK 与 IMU capture，输出 JSONL。 |
| `rscap_v2/pipeline_common*.py` | rscap 解析、时间处理和采集格式兼容的共用逻辑。 |
模式3和4应让同一墙面、立柱、路缘和地面尽量重合。终端同时打印 `B^-1(X^-1AX)` 的平移和旋转增量。应查看多对，不能只挑视觉效果最好的一对。
### run：推荐 PowerShell 入口
## 7. data4参考结果
| 文件 | 职责 |
|---|---|
| `export_legacy_stations.ps1` | 批量导出 LiDAR 与 RTK 同在逐站 dlog 中的旧格式。 |
| `export_multisensor_stations.ps1` | 解析独立 RTK/IMU，导出逐站 LiDAR，并按 LiDAR 帧建立关联。 |
| `prepare_legacy_dataset.ps1` | 旧式导出结果转换为 prepared。 |
| `prepare_multisensor_dataset.ps1` | combined 多传感器结果转换为 prepared。 |
| `run_single_dataset.ps1` | 单批数据同时完成两个 GICP 后端、B 筛选、consensus 和 X 求解。 |
| `run_all.ps1` | 历史流程：第二批求解，第一批稀疏 RTK 数据作辅助复核。 |
| `run_consensus_finish.ps1` | 在已有两个后端 B 的基础上重新生成 consensus 和汇总结果。 |
| `run_sensitivity_scan.ps1` | 对历史运动对执行外参角度灵敏度扫描。 |
| `view_result.ps1` | 传入匹配的 frames、B 和 X，运行交互式 3D 可视化。 |
### 标定核心文件的数据关系
仓库保留了精简参考产物，见[`results/reference_data4`](results/reference_data4/README.md)：
```text
原始 dlog/rscap
-> tools 导出和时间关联
-> prepared/{frames_all, body_poses_*.csv}
-> rigorous_calibration.py pairs 生成 A、B
-> refine_pairs.py 做与 X 无关的 B 筛选
-> cross_backend_filter.py 生成 consensus B
-> rigorous_calibration.py calibrate 联合 AX=XB 与地面约束求 X
-> validate / visualize_pair_3d.py 做数值和三维检查
translation_m = [1.638179350, -0.240844799, 0.084481236]
RPY_deg_xyz = [-0.817167459, 1.323288119, -22.104163318]
站点：34
共识运动对：25
AX Translation RMS：0.100207 m
AX Rotation RMS：1.252794°
Weighted Jacobian condition：7.739413
Open3D vs small_gicp：0.003889 m / 0.188431°
```
唯一建议下游读取的参考结果是[`final_T_RTK_lidar.json`](results/reference_data4/final_T_RTK_lidar.json)。
## 重要限制
## 8. z与精度限制
- RTK 车体姿态当前是双天线 heading 构造的 yaw-only 轨迹；没有用 RTK pitch/roll 构造 A。
- 新式解析器保存 IMU 与 RTK pitch 等原始字段，但当前手眼方程未融合 IMU。
- 静止站点法不估计 LiDAR–RTK 时间偏移；时间戳关联必须在导出阶段通过审计。
- 地面约束负责 roll、pitch 和 z 的补充可观性，不会独立求出另一套六自由度外参。
- 仓库归档的是结果和轻量 B 文件，不包含云盘中的原始点云数据。
平面阿克曼运动不能独立观测z。参考结果使用34站地面平面和RTK参考点离地`0.8535 m`约束z；该高度必须量到实际GGA参考点/天线相位中心。更改参考高度后必须重新求解。
## 专题说明
AX残差、Hessian/Jacobian条件数、bootstrap和双后端一致性只证明内部一致性，不能单独证明逐帧GT达到±3 cm。当前关联仍以LiDAR和串口主机接收时间为主；GNSS周/周内时间和IMU设备时间被保留，但没有联合估计时钟偏移与漂移。用于连续GT pose前，应补做严格设备时间同步和独立轨迹验证。
- [运动对诊断](PAIR_DIAGNOSTICS.md)
- [双后端共识筛选](CONSENSUS_SELECTION.md)
- [结果文件索引](results/README.md)
此外，代码无法单独证明GGA对应哪根物理天线、`rawHeading`是ANT1→ANT2还是ANT2→ANT1；必须用接收机配置、接线和现场运动实验确认。方向错误会导致RTK坐标系yaw相差约180°。
## 9. 仓库目录
| 目录 | 职责 |
|---|---|
| [`code/`](code/) | GICP、运动对质量评价、AX=XB求解、结果封装和3D可视化 |
| [`tools/`](tools/) | 原始dlog/rscap解析、按LiDAR帧关联及静态站点prepared生成 |
| [`run/`](run/) | PowerShell入口；所有数据和输出路径都通过参数传入 |
| [`results/reference_data4/`](results/reference_data4/) | 可提交Git的精简参考结果，不包含点云和本机过程目录 |
| `work/`、`outputs/` | 本地运行生成物，已由`.gitignore`排除 |
各代码文件职责见[`code/README.md`](code/README.md)，命令索引见[`run/README.md`](run/README.md)，工具说明见[`tools/README.md`](tools/README.md)。
LiDAR_RTK_Direct_Calibration/code/README.md → code/README.md
View File
code/compare_extrinsics.py
+1 -1
View File
@@ -1,5 +1,5 @@
#!/usr/bin/env python3
"""Compare two T_body_lidar JSON files in parameter space and on SE(3)."""
"""Compare two homogeneous-extrinsic JSON files in parameter space and on SE(3)."""
import argparse
import json
code/finalize_consensus.py
-58
View File
@@ -1,58 +0,0 @@
#!/usr/bin/env python3
"""Publish the cross-backend-consensus result as the recommended deliverable."""
import argparse
import json
from pathlib import Path
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--consensus-extrinsic", required=True)
parser.add_argument("--consensus-check", required=True)
parser.add_argument("--open3d-extrinsic", required=True)
parser.add_argument("--small-extrinsic", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--summary", required=True)
args = parser.parse_args()
consensus = json.loads(Path(args.consensus_extrinsic).read_text(encoding="utf-8-sig"))
check = json.loads(Path(args.consensus_check).read_text(encoding="utf-8-sig"))
open3d = json.loads(Path(args.open3d_extrinsic).read_text(encoding="utf-8-sig"))
small = json.loads(Path(args.small_extrinsic).read_text(encoding="utf-8-sig"))
summary = {
"recommended_method": "Open3D B gated by Open3D-small_gicp cross-backend agreement",
"selection_is_X_independent": True,
"second_batch_role": "estimation (dense RTK)",
"first_batch_role": "auxiliary check only (sparse RTK)",
"consensus": {
"translation_m": consensus["translation_m"],
"rotation_rpy_deg_xyz": consensus["rotation_rpy_deg_xyz"],
"estimation": consensus["estimation"]["residuals"],
"bootstrap_std": consensus["bootstrap"]["std"],
"batch1_auxiliary": check["metrics"],
},
"separate_backend_results": {
"open3d_gicp": {
"translation_m": open3d["translation_m"],
"rotation_rpy_deg_xyz": open3d["rotation_rpy_deg_xyz"],
},
"small_gicp": {
"translation_m": small["translation_m"],
"rotation_rpy_deg_xyz": small["rotation_rpy_deg_xyz"],
},
},
"warning": "AX rotation RMS remains about one degree; this is not centimetre-grade absolute certification.",
}
published = dict(consensus)
published["selection"] = {
"method": summary["recommended_method"],
"selection_is_X_independent": True,
"consensus_pair_threshold": "Open3D-small_gicp B delta <= 0.05 m and <= 0.50 deg",
"warning": summary["warning"],
}
Path(args.output).write_text(json.dumps(published, ensure_ascii=False, indent=2), encoding="utf-8")
Path(args.summary).write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
print(json.dumps(summary, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/code/finalize_direct_rtk_lidar.py → code/finalize_direct_rtk_lidar.py
View File
code/rigorous_calibration.py
+19 -17
View File
@@ -1,8 +1,9 @@
#!/usr/bin/env python3
"""Rigorous stationary LiDAR / dual-antenna RTK hand-eye calibration.
"""Rigorous stationary LiDAR / reference-trajectory hand-eye calibration.
Convention: T_A_B maps points from frame B into frame A.
X = T_body_lidar, A_ij = T_W_Bi^-1 T_W_Bj, B_ij = T_Li_Lj,
For this repository the reference frame is the RTK navigation frame.
X = T_RTK_lidar, A_ij = T_W_Ri^-1 T_W_Rj, B_ij = T_Li_Lj,
therefore A_ij X = X B_ij. Raw sensor-frame points_raw are used.
"""
from __future__ import annotations
@@ -367,17 +368,17 @@ def cmd_pairs(args):
stations = load_stations(
args.frames, args.min_range, args.max_range, args.z_min, args.z_max
)
body = read_poses(args.body)
reference = read_poses(args.reference_poses)
if len(stations) < args.min_stations:
raise ValueError(f"need at least {args.min_stations} stations, got {len(stations)}")
body_poses, body_dt = [], []
reference_poses, reference_dt = [], []
for timestamp, _, _, xyz in stations:
if len(xyz) < args.min_roi_points:
raise ValueError(f"station at {timestamp} has only {len(xyz)} ROI points")
pose, dt = nearest_pose(body, timestamp + args.time_offset)
body_poses.append(pose)
body_dt.append(dt)
body_poses = np.asarray(body_poses)
pose, dt = nearest_pose(reference, timestamp + args.time_offset)
reference_poses.append(pose)
reference_dt.append(dt)
reference_poses = np.asarray(reference_poses)
split = [split_holdout(station[3], args.holdout_fraction, i)
for i, station in enumerate(stations)]
rng = np.random.default_rng(args.seed)
@@ -385,7 +386,7 @@ def cmd_pairs(args):
accepted_transforms = {}
for i in range(len(stations)):
for j in range(i + args.min_gap, min(len(stations), i + args.max_gap + 1)):
a_ij = inverse_transform(body_poses[i]) @ body_poses[j]
a_ij = inverse_transform(reference_poses[i]) @ reference_poses[j]
translation = float(np.linalg.norm(a_ij[:2, 3]))
rotation = rotation_angle_deg(a_ij[:3, :3])
if translation < args.min_translation and rotation < args.min_rotation:
@@ -445,7 +446,7 @@ def cmd_pairs(args):
"lidar_time_i": stations[i][0], "lidar_time_j": stations[j][0],
"frame_counter_i": stations[i][1], "frame_counter_j": stations[j][1],
"rtk_translation_m": translation, "rtk_rotation_deg": rotation,
"nearest_rtk_dt_i_s": body_dt[i], "nearest_rtk_dt_j_s": body_dt[j],
"nearest_rtk_dt_i_s": reference_dt[i], "nearest_rtk_dt_j_s": reference_dt[j],
"initial_B_source": "X0=identity; B0=A (no measured extrinsic)",
"B_ij_4x4": forward["transform"].tolist(),
"backend": args.backend, "backend_converged": forward["converged"],
@@ -474,7 +475,7 @@ def cmd_pairs(args):
output, A=np.asarray(accepted_a), B=np.asarray(accepted_b),
meta=np.asarray(accepted_meta),
station_times=np.asarray([item[0] for item in stations]),
rtk_nearest_dt_s=np.asarray(body_dt), backend=np.asarray(args.backend),
rtk_nearest_dt_s=np.asarray(reference_dt), backend=np.asarray(args.backend),
)
quality = {
"schema_version": 2,
@@ -556,7 +557,7 @@ def calibration_residual(params, a_array, b_array, planes, args):
normal_body = x[:3, :3] @ plane[:3]
values.extend((np.cross(normal_body, body_up) / args.plane_normal_sigma).tolist())
body_distance = plane[3] - float(normal_body @ x[:3, 3])
values.append((body_distance - args.body_height) / args.plane_height_sigma)
values.append((body_distance - args.reference_height) / args.plane_height_sigma)
return np.asarray(values)
@@ -644,7 +645,7 @@ def cmd_calibrate(args):
"schema_version": 2,
"success": bool(best.success),
"message": best.message,
"convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame",
"convention": "T_reference_lidar maps raw LiDAR points into the supplied reference frame",
"equation": "A_ij X = X B_ij",
"measured_extrinsic_used_as_initial": False,
"translation_m": x[:3, 3].tolist(),
@@ -655,8 +656,8 @@ def cmd_calibrate(args):
"residuals": pair_metrics(a_array, b_array, x)},
"ground": {
"planes": len(planes),
"body_origin_height_above_ground_m": args.body_height,
"formula": "d_lidar - (R_X n_lidar)^T t_X - body_height",
"reference_origin_height_above_ground_m": args.reference_height,
"formula": "d_lidar - (R_X n_lidar)^T t_X - reference_height",
},
"linearized_one_sigma": {
"translation_m": sigma[:3].tolist(),
@@ -713,7 +714,8 @@ def build_parser():
pairs = commands.add_parser("pairs")
pairs.add_argument("--backend", choices=["open3d", "small_gicp"], required=True)
pairs.add_argument("--frames", required=True); pairs.add_argument("--body", required=True)
pairs.add_argument("--frames", required=True)
pairs.add_argument("--reference-poses", "--body", dest="reference_poses", required=True)
pairs.add_argument("--output", required=True); pairs.add_argument("--quality-json"); pairs.add_argument("--quality-csv")
pairs.add_argument("--time-offset", type=float, default=0.0)
pairs.add_argument("--min-stations", type=int, default=30); pairs.add_argument("--min-pairs", type=int, default=25)
@@ -746,7 +748,7 @@ def build_parser():
calibrate.add_argument("--rotation-sigma", type=float, default=0.5)
calibrate.add_argument("--plane-normal-sigma", type=float, default=0.02)
calibrate.add_argument("--plane-height-sigma", type=float, default=0.03)
calibrate.add_argument("--body-height", type=float, default=0.2335)
calibrate.add_argument("--reference-height", "--body-height", dest="reference_height", type=float, default=0.8535)
calibrate.add_argument("--solver-multistart", type=int, default=12)
calibrate.add_argument("--start-translation-sigma", type=float, default=1.0)
calibrate.add_argument("--start-rotation-sigma", type=float, default=20.0)
code/scan_extrinsic_sensitivity.py
-311
View File
@@ -1,311 +0,0 @@
#!/usr/bin/env python3
"""Scan body-left RPY corrections locally and validate them over every B pair.
This command is diagnostic only. It never writes or replaces an extrinsic JSON.
"""
from __future__ import annotations
import argparse
import csv
import json
import math
from pathlib import Path
import numpy as np
from scipy.spatial.transform import Rotation
from rigorous_calibration import (
inverse_transform, read_pairs, read_planes, rotation_angle_deg, rpy_deg,
)
def statistics(values):
values = np.asarray(values, float)
return {
"rms": float(np.sqrt(np.mean(values ** 2))),
"median": float(np.median(values)),
"p90": float(np.quantile(values, 0.90)),
"p95": float(np.quantile(values, 0.95)),
"max": float(np.max(values)),
}
def body_left_rpy(x, rpy_correction_deg):
correction = np.eye(4)
correction[:3, :3] = Rotation.from_euler(
"xyz", np.asarray(rpy_correction_deg, float), degrees=True
).as_matrix()
return correction @ x
def pair_delta(a_ij, b_ij, x):
predicted = inverse_transform(x) @ a_ij @ x
delta = inverse_transform(b_ij) @ predicted
translation = np.asarray(delta[:3, 3], float)
return {
"translation_xyz_m": translation.tolist(),
"translation_xyz_cm": (100.0 * translation).tolist(),
"translation_norm_m": float(np.linalg.norm(translation)),
"rotation_rpy_deg_xyz": rpy_deg(delta[:3, :3]),
"rotation_angle_deg": rotation_angle_deg(delta[:3, :3]),
}
def ground_metrics(planes, x, body_height):
if len(planes) == 0:
return None
up = np.array([0.0, 0.0, 1.0])
tilt_deg, height_m = [], []
for plane in planes:
normal_body = x[:3, :3] @ plane[:3]
normal_body /= np.linalg.norm(normal_body)
tilt_deg.append(math.degrees(math.atan2(
np.linalg.norm(np.cross(normal_body, up)),
float(np.clip(normal_body @ up, -1.0, 1.0)),
)))
height_m.append(
float(plane[3] - normal_body @ x[:3, 3] - body_height)
)
return {
"normal_tilt_deg": statistics(tilt_deg),
"height_residual_m": statistics(height_m),
}
def evaluate(label, correction, a_array, b_array, meta, x, pair_index,
translation_scale, rotation_scale, planes, body_height):
candidate_x = body_left_rpy(x, correction)
per_pair = []
translation, rotation, normalized = [], [], []
for index, (a_ij, b_ij, pair_meta) in enumerate(zip(a_array, b_array, meta)):
item = pair_delta(a_ij, b_ij, candidate_x)
item.update({
"pair_index": index,
"i": int(pair_meta[0]),
"j": int(pair_meta[1]),
})
t = item["translation_norm_m"]
r = item["rotation_angle_deg"]
translation.append(t)
rotation.append(r)
normalized.append(math.hypot(t / translation_scale, r / rotation_scale))
per_pair.append(item)
return {
"label": label,
"body_left_rpy_correction_deg_xyz": list(map(float, correction)),
"candidate_extrinsic": {
"translation_m": candidate_x[:3, 3].tolist(),
"rotation_rpy_deg_xyz": rpy_deg(candidate_x[:3, :3]),
},
"all_pairs": {
"count": len(per_pair),
"translation_m": statistics(translation),
"rotation_deg": statistics(rotation),
"normalized_pair_score": statistics(normalized),
"normalized_global_rms": float(np.sqrt(np.mean(np.asarray(normalized) ** 2))),
},
"selected_pair": per_pair[pair_index],
"ground": ground_metrics(planes, candidate_x, body_height),
"per_pair": per_pair,
}
def candidate_grid(pitch_values, roll_values, yaw_values):
answer = [("baseline", (0.0, 0.0, 0.0))]
for pitch in pitch_values:
answer.append((f"pitch_{pitch:+.3f}", (0.0, pitch, 0.0)))
for pitch in (0.0, *pitch_values):
for roll in roll_values:
answer.append((
f"pitch_{pitch:+.3f}_roll_{roll:+.3f}",
(roll, pitch, 0.0),
))
for yaw in yaw_values:
answer.append((f"yaw_{yaw:+.3f}_diagnostic", (0.0, 0.0, yaw)))
unique = []
seen = set()
for label, values in answer:
key = tuple(round(float(value), 12) for value in values)
if key not in seen:
seen.add(key)
unique.append((label, values))
return unique
def z_observability(a_array, x, test_shift_m):
shift = np.eye(4)
shift[2, 3] = test_shift_m
shifted_x = shift @ x
effects = []
for a_ij in a_array:
before = inverse_transform(x) @ a_ij @ x
after = inverse_transform(shifted_x) @ a_ij @ shifted_x
delta = inverse_transform(before) @ after
effects.append((
float(np.linalg.norm(delta[:3, 3])),
rotation_angle_deg(delta[:3, :3]),
))
effects = np.asarray(effects, float)
maximum = np.max(effects, axis=0)
return {
"body_left_z_test_shift_m": test_shift_m,
"max_predicted_motion_change_translation_m": float(maximum[0]),
"max_predicted_motion_change_rotation_deg": float(maximum[1]),
"numerically_unobservable": bool(maximum[0] < 1e-10 and maximum[1] < 1e-10),
"note": "AX pairs cannot determine X.z when every A rotation preserves body Z; use ground/external height constraints.",
}
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--pairs", required=True)
parser.add_argument("--extrinsic", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--csv")
parser.add_argument("--ground-planes")
parser.add_argument("--pair-index", type=int, default=0)
parser.add_argument("--pitch-values", nargs="+", type=float, default=[0.1, 0.2, 0.3])
parser.add_argument("--roll-values", nargs="+", type=float, default=[-0.2, -0.1, 0.1])
parser.add_argument("--yaw-values", nargs="+", type=float, default=[-0.2, 0.2])
parser.add_argument("--translation-scale", type=float, default=0.05)
parser.add_argument("--rotation-scale", type=float, default=0.5)
parser.add_argument("--body-height", type=float, default=0.2335)
args = parser.parse_args()
a_array, b_array, meta, stations = read_pairs(args.pairs)
if not 0 <= args.pair_index < len(a_array):
raise IndexError(f"pair-index {args.pair_index} outside [0,{len(a_array)-1}]")
with Path(args.extrinsic).open(encoding="utf-8-sig") as stream:
x = np.asarray(json.load(stream)["matrix_4x4"], float)
planes = read_planes(args.ground_planes) if args.ground_planes else np.empty((0, 4))
candidates = [
evaluate(
label, correction, a_array, b_array, meta, x, args.pair_index,
args.translation_scale, args.rotation_scale, planes, args.body_height,
)
for label, correction in candidate_grid(
args.pitch_values, args.roll_values, args.yaw_values
)
]
baseline = candidates[0]
baseline_scores = np.asarray([
math.hypot(
item["translation_norm_m"] / args.translation_scale,
item["rotation_angle_deg"] / args.rotation_scale,
)
for item in baseline["per_pair"]
])
base_global = baseline["all_pairs"]["normalized_global_rms"]
for candidate in candidates:
scores = np.asarray([
math.hypot(
item["translation_norm_m"] / args.translation_scale,
item["rotation_angle_deg"] / args.rotation_scale,
)
for item in candidate["per_pair"]
])
delta = scores - baseline_scores
candidate["comparison_to_baseline"] = {
"normalized_global_rms_change": float(
candidate["all_pairs"]["normalized_global_rms"] - base_global
),
"improved_pairs": int(np.sum(delta < -1e-12)),
"worsened_pairs": int(np.sum(delta > 1e-12)),
"unchanged_pairs": int(np.sum(np.abs(delta) <= 1e-12)),
"median_per_pair_score_change": float(np.median(delta)),
"global_consistency_signal": bool(
candidate["all_pairs"]["normalized_global_rms"] < base_global
and np.sum(delta < -1e-12) > np.sum(delta > 1e-12)
),
}
ranking = sorted(
candidates,
key=lambda item: item["all_pairs"]["normalized_global_rms"],
)
report = {
"schema_version": 1,
"diagnostic_only": True,
"extrinsic_was_modified": False,
"equation": "delta_ij = B_ij^-1 * (X^-1 * A_ij * X)",
"correction_convention": "X_test = DeltaR_body * X; DeltaR uses fixed body xyz RPY axes",
"component_frame": "delta translation/RPY components are in station-j LiDAR coordinates, not screen axes",
"selection_rule": (
"Never accept a correction from selected_pair alone. Require improvement over all "
"refined pairs, directional consistency across pairs, acceptable ground constraints, "
"and independent visual review. This script never overwrites X."
),
"pairs_file": str(Path(args.pairs).resolve()),
"extrinsic_file": str(Path(args.extrinsic).resolve()),
"stations": stations,
"pairs": len(a_array),
"selected_pair_index": args.pair_index,
"selected_pair_stations": [int(meta[args.pair_index, 0]), int(meta[args.pair_index, 1])],
"normalization": {
"translation_scale_m": args.translation_scale,
"rotation_scale_deg": args.rotation_scale,
},
"z_observability": z_observability(a_array, x, 0.10),
"ranking_by_all_pair_normalized_rms": [
{
"rank": rank,
"label": item["label"],
"body_left_rpy_correction_deg_xyz": item["body_left_rpy_correction_deg_xyz"],
"normalized_global_rms": item["all_pairs"]["normalized_global_rms"],
**item["comparison_to_baseline"],
}
for rank, item in enumerate(ranking, 1)
],
"candidates": candidates,
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(report, ensure_ascii=False, indent=2), encoding="utf-8")
csv_path = Path(args.csv) if args.csv else output.with_suffix(".csv")
with csv_path.open("w", encoding="utf-8", newline="") as stream:
fields = [
"label", "roll_correction_deg", "pitch_correction_deg", "yaw_correction_deg",
"selected_pair_translation_cm", "selected_pair_rotation_deg",
"all_pair_translation_rms_m", "all_pair_rotation_rms_deg",
"normalized_global_rms", "normalized_global_rms_change",
"improved_pairs", "worsened_pairs", "global_consistency_signal",
"ground_normal_tilt_rms_deg", "ground_height_rms_m",
]
writer = csv.DictWriter(stream, fieldnames=fields)
writer.writeheader()
for item in candidates:
correction = item["body_left_rpy_correction_deg_xyz"]
ground = item["ground"]
comparison = item["comparison_to_baseline"]
writer.writerow({
"label": item["label"],
"roll_correction_deg": correction[0],
"pitch_correction_deg": correction[1],
"yaw_correction_deg": correction[2],
"selected_pair_translation_cm": item["selected_pair"]["translation_norm_m"] * 100.0,
"selected_pair_rotation_deg": item["selected_pair"]["rotation_angle_deg"],
"all_pair_translation_rms_m": item["all_pairs"]["translation_m"]["rms"],
"all_pair_rotation_rms_deg": item["all_pairs"]["rotation_deg"]["rms"],
"normalized_global_rms": item["all_pairs"]["normalized_global_rms"],
"normalized_global_rms_change": comparison["normalized_global_rms_change"],
"improved_pairs": comparison["improved_pairs"],
"worsened_pairs": comparison["worsened_pairs"],
"global_consistency_signal": comparison["global_consistency_signal"],
"ground_normal_tilt_rms_deg": None if ground is None else ground["normal_tilt_deg"]["rms"],
"ground_height_rms_m": None if ground is None else ground["height_residual_m"]["rms"],
})
print(json.dumps({
"diagnostic_only": True,
"selected_pair": baseline["selected_pair"],
"z_observability": report["z_observability"],
"top_all_pair_candidates": report["ranking_by_all_pair_normalized_rms"][:8],
"output": str(output.resolve()),
"csv": str(csv_path.resolve()),
}, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
code/summarize_results.py
-87
View File
@@ -1,87 +0,0 @@
#!/usr/bin/env python3
"""Build a concise backend comparison and select the recommended result."""
import argparse
import json
from pathlib import Path
import numpy as np
from scipy.spatial.transform import Rotation
def main():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--open3d", required=True)
parser.add_argument("--small", required=True)
parser.add_argument("--open3d-quality", required=True)
parser.add_argument("--small-quality", required=True)
parser.add_argument("--open3d-check", required=True)
parser.add_argument("--small-check", required=True)
parser.add_argument("--output", required=True)
parser.add_argument("--recommended-output", required=True)
args = parser.parse_args()
open_result = json.loads(Path(args.open3d).read_text(encoding="utf-8-sig"))
small_result = json.loads(Path(args.small).read_text(encoding="utf-8-sig"))
open_quality = json.loads(Path(args.open3d_quality).read_text(encoding="utf-8-sig"))
small_quality = json.loads(Path(args.small_quality).read_text(encoding="utf-8-sig"))
open_check = json.loads(Path(args.open3d_check).read_text(encoding="utf-8-sig"))
small_check = json.loads(Path(args.small_check).read_text(encoding="utf-8-sig"))
x_open = np.asarray(open_result["matrix_4x4"], float)
x_small = np.asarray(small_result["matrix_4x4"], float)
delta = np.linalg.inv(x_open) @ x_small
def compact(result, quality, check):
estimate = result["estimation"]["residuals"]
auxiliary = check["metrics"]
return {
"translation_m": result["translation_m"],
"rotation_rpy_deg_xyz": result["rotation_rpy_deg_xyz"],
"estimation_pairs": estimate["pairs"],
"estimation_translation_rms_m": estimate["translation_m"]["rms"],
"estimation_rotation_rms_deg": estimate["rotation_deg"]["rms"],
"bootstrap_std": result["bootstrap"]["std"],
"initial_B_loop_closure": quality["accepted_loop_closure"],
"batch1_auxiliary_pairs": auxiliary["pairs"],
"batch1_auxiliary_translation_rms_m": auxiliary["translation_m"]["rms"],
"batch1_auxiliary_rotation_rms_deg": auxiliary["rotation_deg"]["rms"],
}
summary = {
"recommended_backend": "open3d_gicp",
"selection_reason": (
"The two X estimates agree closely; Open3D has lower second-batch AX residual, "
"better B loop closure, and lower first-batch auxiliary residual."
),
"coordinate_convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame",
"measured_extrinsic_used_as_initial": False,
"second_batch_role": "estimation (dense RTK)",
"first_batch_role": "auxiliary check only (sparse RTK)",
"backend_difference": {
"translation_m": float(np.linalg.norm(delta[:3, 3])),
"rotation_deg": float(np.rad2deg(Rotation.from_matrix(delta[:3, :3]).magnitude())),
},
"open3d_gicp": compact(open_result, open_quality, open_check),
"small_gicp": compact(small_result, small_quality, small_check),
"important_limit": (
"Backend agreement is strong, but AX rotation RMS remains about one degree. "
"This is not a centimetre-grade absolute certification."
),
}
output = Path(args.output)
output.parent.mkdir(parents=True, exist_ok=True)
output.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding="utf-8")
recommended = dict(open_result)
recommended["selection"] = {
"recommended_backend": "open3d_gicp",
"comparison_summary": str(output.name),
"backend_difference": summary["backend_difference"],
"warning": summary["important_limit"],
}
Path(args.recommended_output).write_text(
json.dumps(recommended, ensure_ascii=False, indent=2), encoding="utf-8"
)
print(json.dumps(summary, ensure_ascii=False, indent=2))
if __name__ == "__main__":
main()
LiDAR_RTK_Direct_Calibration/data/README.md → data/README.md
View File
requirements.txt
+1 -1
View File
@@ -1,4 +1,4 @@
numpy>=1.26
scipy>=1.11
open3d>=0.18
small-gicp==1.0.1
small-gicp
results/01_previous_two_batches/README.md
-10
View File
@@ -1,10 +0,0 @@
# 历史两批结果
第二批 38 站的密集 RTK 数据用于求解；第一批 38 站因 RTK 约 10 秒一条，仅作辅助检查。最终部署值：
```text
translation_m = [1.297759692, -0.000067331, 0.720497835]
RPY_deg_xyz = [-0.785151146, 1.202660822, -0.835510053]
```
共识估计使用 39 个运动对，AX RMS 约 `0.07985 m / 0.96118°`。第一批辅助检查 22 对约 `0.06067 m / 1.00298°`。`final_extrinsic_deployment.json` 是唯一建议直接交给下游的部署 JSON；其余文件用于审计和复现。
results/01_previous_two_batches/common/ground_planes_batch2.csv
-39
View File
@@ -1,39 +0,0 @@
time,nx,ny,nz,d,inliers,rms_m,frame_counter
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1 time nx ny nz d inliers rms_m frame_counter
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results/01_previous_two_batches/comparison_summary.json
-79
View File
@@ -1,79 +0,0 @@
{
"recommended_backend": "open3d_gicp",
"selection_reason": "The two X estimates agree closely; Open3D has lower second-batch AX residual, better B loop closure, and lower first-batch auxiliary residual.",
"coordinate_convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame",
"measured_extrinsic_used_as_initial": false,
"second_batch_role": "estimation (dense RTK)",
"first_batch_role": "auxiliary check only (sparse RTK)",
"backend_difference": {
"translation_m": 0.00181035475331947,
"rotation_deg": 0.06312194455789993
},
"open3d_gicp": {
"translation_m": [
1.2978831677200011,
-0.0030997734098957433,
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],
"rotation_rpy_deg_xyz": [
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],
"estimation_pairs": 66,
"estimation_translation_rms_m": 0.10017183831403174,
"estimation_rotation_rms_deg": 1.011529772202344,
"bootstrap_std": [
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0.001427746471392659,
0.07576290622767821,
0.06304875844333606,
0.07843172452171372
],
"initial_B_loop_closure": {
"count": 106,
"translation_rms_m": 0.020350848901853437,
"translation_p95_m": 0.04464367942375473,
"rotation_rms_deg": 0.2674546762953808,
"rotation_p95_deg": 0.5761692075739222
},
"batch1_auxiliary_pairs": 41,
"batch1_auxiliary_translation_rms_m": 0.06627076507951374,
"batch1_auxiliary_rotation_rms_deg": 1.0783402768758907
},
"small_gicp": {
"translation_m": [
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-0.0035647011612625953,
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],
"rotation_rpy_deg_xyz": [
-0.7871563544979889,
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-0.9357571721886107
],
"estimation_pairs": 80,
"estimation_translation_rms_m": 0.11870623426762615,
"estimation_rotation_rms_deg": 1.135091439337232,
"bootstrap_std": [
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0.06849719260762097,
0.05792635838163369,
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],
"initial_B_loop_closure": {
"count": 252,
"translation_rms_m": 0.06356464456110195,
"translation_p95_m": 0.12253135158224471,
"rotation_rms_deg": 0.7286536542725655,
"rotation_p95_deg": 1.4911215410143956
},
"batch1_auxiliary_pairs": 36,
"batch1_auxiliary_translation_rms_m": 0.07402020402512643,
"batch1_auxiliary_rotation_rms_deg": 1.1492603033421918
},
"important_limit": "Backend agreement is strong, but AX rotation RMS remains about one degree. This is not a centimetre-grade absolute certification."
}
results/01_previous_two_batches/consensus/B_batch1_consensus.consensus.json
-328
View File
@@ -1,328 +0,0 @@
{
"selection_is_X_independent": true,
"B_source": "Open3D; small_gicp is used only as an agreement gate",
"max_translation_m": 0.05,
"max_rotation_deg": 0.5,
"input_open3d_pairs": 41,
"accepted_pairs": 22,
"pairs": [
{
"i": 0,
"j": 1,
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"open3d_small_rotation_deg": 0.8091245488258404,
"accepted": false,
"reason": "backend_disagreement"
},
{
"i": 0,
"j": 3,
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"open3d_small_rotation_deg": 0.14377171116926005,
"accepted": true,
"reason": ""
},
{
"i": 1,
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"open3d_small_rotation_deg": 0.09198739647809913,
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"reason": ""
},
{
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},
{
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},
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},
{
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},
{
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results/01_previous_two_batches/consensus/B_batch1_consensus.npz
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results/01_previous_two_batches/consensus/B_batch2_consensus.consensus.json
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results/01_previous_two_batches/consensus/B_batch2_consensus.npz
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results/01_previous_two_batches/consensus/extrinsic_batch2_consensus.json
-399
View File
@@ -1,399 +0,0 @@
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results/01_previous_two_batches/diagnostics/consensus_pair0_left_rpy_scan.csv
-19
View File
@@ -1,19 +0,0 @@
label,roll_correction_deg,pitch_correction_deg,yaw_correction_deg,selected_pair_translation_cm,selected_pair_rotation_deg,all_pair_translation_rms_m,all_pair_rotation_rms_deg,normalized_global_rms,normalized_global_rms_change,improved_pairs,worsened_pairs,global_consistency_signal,ground_normal_tilt_rms_deg,ground_height_rms_m
baseline,0.0,0.0,0.0,13.53760421849012,0.5166365681634405,0.07984780651249465,0.961176171904301,2.4991413356769443,0.0,0,0,False,1.6662748432297785,0.03471396517121729
pitch_+0.100,0.0,0.1,0.0,13.322368374959654,0.4851311566747633,0.07937622701088871,0.9709178397492695,2.508178616440067,0.009037280763122713,17,22,False,1.661255702204205,0.03471396517121729
pitch_+0.200,0.0,0.2,0.0,13.107741962217032,0.45594025406310634,0.07905894857986535,0.991843015836615,2.536757223795175,0.03761588811823069,17,22,False,1.6622470214176333,0.03471396517121729
pitch_+0.300,0.0,0.3,0.0,12.89375489636804,0.4295360884271374,0.07889765995849754,1.0232658257505307,2.5842267894606974,0.0850854537837531,16,23,False,1.669238092496052,0.034713965171217276
pitch_+0.000_roll_-0.200,-0.2,0.0,0.0,13.395035432680281,0.501501180601739,0.08136434456273667,0.9671396098919116,2.5277457796497282,0.028604443972783944,12,27,False,1.7107124278398285,0.034713965171217304
pitch_+0.000_roll_-0.100,-0.1,0.0,0.0,13.466127879921094,0.5071126466746416,0.08054864611850543,0.958376027630505,2.503831503507573,0.004690167830628589,13,26,False,1.685676496509504,0.034713965171217304
pitch_+0.000_roll_+0.100,0.1,0.0,0.0,13.609457775458583,0.5298619890141043,0.07926481666577584,0.9754404878748625,2.5137822192795105,0.014640883602566213,23,16,False,1.652705907802981,0.034713965171217304
pitch_+0.100_roll_-0.200,-0.2,0.1,0.0,13.180263465884618,0.4689801268726104,0.08100204146829142,0.9768217811391146,2.5379628557651293,0.03882152008818496,14,25,False,1.7058242505297496,0.034713965171217304
pitch_+0.100_roll_-0.100,-0.1,0.1,0.0,13.251119920699841,0.47497594758710626,0.08013195956996022,0.9681458624297539,2.5134991588556583,0.014357823178714035,17,22,False,1.6807154082401394,0.03471396517121729
pitch_+0.100_roll_+0.100,0.1,0.1,0.0,13.394001942784334,0.4991919434350986,0.07873808044179886,0.9850410981186302,2.5221217227950565,0.022980387118112233,19,20,False,1.6476453039285432,0.0347139651712173
pitch_+0.200_roll_-0.200,-0.2,0.2,0.0,12.966120616418092,0.43871598020210434,0.08079188106279227,0.9976230703529106,2.567477045713792,0.0683357100368478,15,24,False,1.7067898021773054,0.034713965171217304
pitch_+0.200_roll_-0.100,-0.1,0.2,0.0,13.036731063691272,0.44511961856905025,0.07986863254178682,0.989129669887256,2.5426579402355864,0.04351660455864215,19,20,False,1.6816953294526065,0.03471396517121731
pitch_+0.200_roll_+0.100,0.1,0.2,0.0,13.179146202690733,0.4708734484159498,0.07836629319834118,1.0056724288764685,2.5499055866185576,0.0507642509416133,19,20,False,1.648644705864238,0.034713965171217304
pitch_+0.300_roll_-0.200,-0.2,0.3,0.0,12.752638042306833,0.4112074527490782,0.08073488194995813,1.0288692761236073,2.6156330810711834,0.11649174539423912,15,24,False,1.7135991876939975,0.03471396517121731
pitch_+0.300_roll_-0.100,-0.1,0.3,0.0,12.822991835578721,0.41803263983275235,0.07976001272785865,1.020636005744607,2.5906476559016074,0.0915063202246631,16,23,False,1.6886059172123598,0.0347139651712173
pitch_+0.300_roll_+0.100,0.1,0.3,0.0,12.964919880829425,0.44535539842024163,0.07815149226179761,1.036676092226784,2.596507573236926,0.09736623755998153,18,21,False,1.6556931398905776,0.034713965171217304
yaw_-0.200_diagnostic,0.0,0.0,-0.2,13.607478813867315,0.5166365681634414,0.08024771376258277,0.961176171904301,2.5042597228312786,0.005118387154334325,18,21,False,1.6662748432297787,0.0347139651712173
yaw_+0.200_diagnostic,0.0,0.0,0.2,13.479715314434312,0.5166365681634427,0.08028135387591602,0.961176171904301,2.5046909681249,0.005549632447955588,13,26,False,1.6662748432297787,0.0347139651712173
1 label roll_correction_deg pitch_correction_deg yaw_correction_deg selected_pair_translation_cm selected_pair_rotation_deg all_pair_translation_rms_m all_pair_rotation_rms_deg normalized_global_rms normalized_global_rms_change improved_pairs worsened_pairs global_consistency_signal ground_normal_tilt_rms_deg ground_height_rms_m
2 baseline 0.0 0.0 0.0 13.53760421849012 0.5166365681634405 0.07984780651249465 0.961176171904301 2.4991413356769443 0.0 0 0 False 1.6662748432297785 0.03471396517121729
3 pitch_+0.100 0.0 0.1 0.0 13.322368374959654 0.4851311566747633 0.07937622701088871 0.9709178397492695 2.508178616440067 0.009037280763122713 17 22 False 1.661255702204205 0.03471396517121729
4 pitch_+0.200 0.0 0.2 0.0 13.107741962217032 0.45594025406310634 0.07905894857986535 0.991843015836615 2.536757223795175 0.03761588811823069 17 22 False 1.6622470214176333 0.03471396517121729
5 pitch_+0.300 0.0 0.3 0.0 12.89375489636804 0.4295360884271374 0.07889765995849754 1.0232658257505307 2.5842267894606974 0.0850854537837531 16 23 False 1.669238092496052 0.034713965171217276
6 pitch_+0.000_roll_-0.200 -0.2 0.0 0.0 13.395035432680281 0.501501180601739 0.08136434456273667 0.9671396098919116 2.5277457796497282 0.028604443972783944 12 27 False 1.7107124278398285 0.034713965171217304
7 pitch_+0.000_roll_-0.100 -0.1 0.0 0.0 13.466127879921094 0.5071126466746416 0.08054864611850543 0.958376027630505 2.503831503507573 0.004690167830628589 13 26 False 1.685676496509504 0.034713965171217304
8 pitch_+0.000_roll_+0.100 0.1 0.0 0.0 13.609457775458583 0.5298619890141043 0.07926481666577584 0.9754404878748625 2.5137822192795105 0.014640883602566213 23 16 False 1.652705907802981 0.034713965171217304
9 pitch_+0.100_roll_-0.200 -0.2 0.1 0.0 13.180263465884618 0.4689801268726104 0.08100204146829142 0.9768217811391146 2.5379628557651293 0.03882152008818496 14 25 False 1.7058242505297496 0.034713965171217304
10 pitch_+0.100_roll_-0.100 -0.1 0.1 0.0 13.251119920699841 0.47497594758710626 0.08013195956996022 0.9681458624297539 2.5134991588556583 0.014357823178714035 17 22 False 1.6807154082401394 0.03471396517121729
11 pitch_+0.100_roll_+0.100 0.1 0.1 0.0 13.394001942784334 0.4991919434350986 0.07873808044179886 0.9850410981186302 2.5221217227950565 0.022980387118112233 19 20 False 1.6476453039285432 0.0347139651712173
12 pitch_+0.200_roll_-0.200 -0.2 0.2 0.0 12.966120616418092 0.43871598020210434 0.08079188106279227 0.9976230703529106 2.567477045713792 0.0683357100368478 15 24 False 1.7067898021773054 0.034713965171217304
13 pitch_+0.200_roll_-0.100 -0.1 0.2 0.0 13.036731063691272 0.44511961856905025 0.07986863254178682 0.989129669887256 2.5426579402355864 0.04351660455864215 19 20 False 1.6816953294526065 0.03471396517121731
14 pitch_+0.200_roll_+0.100 0.1 0.2 0.0 13.179146202690733 0.4708734484159498 0.07836629319834118 1.0056724288764685 2.5499055866185576 0.0507642509416133 19 20 False 1.648644705864238 0.034713965171217304
15 pitch_+0.300_roll_-0.200 -0.2 0.3 0.0 12.752638042306833 0.4112074527490782 0.08073488194995813 1.0288692761236073 2.6156330810711834 0.11649174539423912 15 24 False 1.7135991876939975 0.03471396517121731
16 pitch_+0.300_roll_-0.100 -0.1 0.3 0.0 12.822991835578721 0.41803263983275235 0.07976001272785865 1.020636005744607 2.5906476559016074 0.0915063202246631 16 23 False 1.6886059172123598 0.0347139651712173
17 pitch_+0.300_roll_+0.100 0.1 0.3 0.0 12.964919880829425 0.44535539842024163 0.07815149226179761 1.036676092226784 2.596507573236926 0.09736623755998153 18 21 False 1.6556931398905776 0.034713965171217304
18 yaw_-0.200_diagnostic 0.0 0.0 -0.2 13.607478813867315 0.5166365681634414 0.08024771376258277 0.961176171904301 2.5042597228312786 0.005118387154334325 18 21 False 1.6662748432297787 0.0347139651712173
19 yaw_+0.200_diagnostic 0.0 0.0 0.2 13.479715314434312 0.5166365681634427 0.08028135387591602 0.961176171904301 2.5046909681249 0.005549632447955588 13 26 False 1.6662748432297787 0.0347139651712173
results/01_previous_two_batches/diagnostics/consensus_pair0_left_rpy_scan.json
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results/01_previous_two_batches/diagnostics/open3d_refined_pair0_left_rpy_scan.csv
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label,roll_correction_deg,pitch_correction_deg,yaw_correction_deg,selected_pair_translation_cm,selected_pair_rotation_deg,all_pair_translation_rms_m,all_pair_rotation_rms_deg,normalized_global_rms,normalized_global_rms_change,improved_pairs,worsened_pairs,global_consistency_signal,ground_normal_tilt_rms_deg,ground_height_rms_m
baseline,0.0,0.0,0.0,7.9716047501124,0.5471090190705759,0.10039155672864886,1.0152957563636194,2.855641391442648,0.0,0,0,False,1.6662748432297785,0.03471396517121729
pitch_+0.100,0.0,0.1,0.0,7.877722926634115,0.5242415332114175,0.10031155812336309,1.0345690614552345,2.8820645462820917,0.026423154839443797,27,39,False,1.661255702204205,0.03471396517121729
pitch_+0.200,0.0,0.2,0.0,7.783902534642328,0.5013877557627758,0.10034501725742666,1.0647782869175946,2.9262023505769332,0.07056095913428528,26,40,False,1.6622470214176333,0.03471396517121729
pitch_+0.300,0.0,0.3,0.0,7.69014529426882,0.4785497243321324,0.1004916878666913,1.1050268752403856,2.9872678674890754,0.1316264760464274,25,41,False,1.669238092496052,0.034713965171217276
pitch_+0.000_roll_-0.200,-0.2,0.0,0.0,7.822046878606513,0.5457706857344996,0.10257010426899679,1.0267538544989139,2.9026099336312234,0.04696854218857549,22,44,False,1.7107124278398285,0.034713965171217304
pitch_+0.000_roll_-0.100,-0.1,0.0,0.0,7.896797905766369,0.545955313109902,0.10143505999644054,1.0151691591975842,2.870174601960012,0.014533210517364115,25,41,False,1.685676496509504,0.034713965171217304
pitch_+0.000_roll_+0.100,0.1,0.0,0.0,8.046465471725153,0.5492256932471735,0.09944242267101946,1.0271293323765827,2.859282573021794,0.0036411815791459468,38,28,False,1.652705907802981,0.034713965171217304
pitch_+0.100_roll_-0.200,-0.2,0.1,0.0,7.728092854641688,0.5228446714705329,0.10256719458254226,1.0458160126880083,2.929664876976855,0.07402348553420701,24,42,False,1.7058242505297496,0.034713965171217304
pitch_+0.100_roll_-0.100,-0.1,0.1,0.0,7.802880223772661,0.5230373912267472,0.10139401948155134,1.0344448200154543,2.896999001946205,0.04135761050355713,31,35,False,1.6807154082401394,0.03471396517121729
pitch_+0.100_roll_+0.100,0.1,0.1,0.0,7.952619015892104,0.5264501536349752,0.09932272291285332,1.0461846579706375,2.885136161262053,0.029494769819405242,30,36,False,1.6476453039285432,0.0347139651712173
pitch_+0.200_roll_-0.200,-0.2,0.2,0.0,7.634199171873722,0.49992704876652105,0.10267526429269669,1.0757093910702569,2.9741363007059745,0.11849490926332651,27,39,False,1.7067898021773054,0.034713965171217304
pitch_+0.200_roll_-0.100,-0.1,0.2,0.0,7.709023440769963,0.5001285987059726,0.10146524807256484,1.0646575627535269,2.9414386837539155,0.08579729231126754,30,36,False,1.6816953294526065,0.03471396517121731
pitch_+0.200_roll_+0.100,0.1,0.2,0.0,7.8588344993060915,0.5036965847149959,0.09931755660454081,1.0760678191977628,2.928699135454269,0.07305774401162113,29,37,False,1.648644705864238,0.034713965171217304
pitch_+0.300_roll_-0.200,-0.2,0.3,0.0,7.540367539154748,0.47701910095086286,0.102893833307342,1.1155635852142416,3.035256961724302,0.17961557028165398,25,41,False,1.7135991876939975,0.03471396517121731
pitch_+0.300_roll_-0.100,-0.1,0.3,0.0,7.615229271779279,0.47723032409407057,0.10164837804314657,1.1049105360171487,3.0027098217989345,0.14706843035628658,25,41,False,1.6886059172123598,0.0347139651712173
pitch_+0.300_roll_+0.100,0.1,0.3,0.0,7.7651136462045685,0.48096817789925783,0.09942680753866891,1.1159092500059349,2.989195517179804,0.13355412573715597,28,38,False,1.6556931398905776,0.034713965171217304
yaw_-0.200_diagnostic,0.0,0.0,-0.2,7.9342751812671235,0.5471090190705757,0.10053142254152782,1.0152957563636194,2.8576089058597884,0.0019675144171404924,35,31,False,1.6662748432297787,0.0347139651712173
yaw_+0.200_diagnostic,0.0,0.0,0.2,8.012589151848083,0.547109019070576,0.10091495789660097,1.0152957563636194,2.8630112438600364,0.007369852417388412,18,48,False,1.6662748432297787,0.0347139651712173
1 label roll_correction_deg pitch_correction_deg yaw_correction_deg selected_pair_translation_cm selected_pair_rotation_deg all_pair_translation_rms_m all_pair_rotation_rms_deg normalized_global_rms normalized_global_rms_change improved_pairs worsened_pairs global_consistency_signal ground_normal_tilt_rms_deg ground_height_rms_m
2 baseline 0.0 0.0 0.0 7.9716047501124 0.5471090190705759 0.10039155672864886 1.0152957563636194 2.855641391442648 0.0 0 0 False 1.6662748432297785 0.03471396517121729
3 pitch_+0.100 0.0 0.1 0.0 7.877722926634115 0.5242415332114175 0.10031155812336309 1.0345690614552345 2.8820645462820917 0.026423154839443797 27 39 False 1.661255702204205 0.03471396517121729
4 pitch_+0.200 0.0 0.2 0.0 7.783902534642328 0.5013877557627758 0.10034501725742666 1.0647782869175946 2.9262023505769332 0.07056095913428528 26 40 False 1.6622470214176333 0.03471396517121729
5 pitch_+0.300 0.0 0.3 0.0 7.69014529426882 0.4785497243321324 0.1004916878666913 1.1050268752403856 2.9872678674890754 0.1316264760464274 25 41 False 1.669238092496052 0.034713965171217276
6 pitch_+0.000_roll_-0.200 -0.2 0.0 0.0 7.822046878606513 0.5457706857344996 0.10257010426899679 1.0267538544989139 2.9026099336312234 0.04696854218857549 22 44 False 1.7107124278398285 0.034713965171217304
7 pitch_+0.000_roll_-0.100 -0.1 0.0 0.0 7.896797905766369 0.545955313109902 0.10143505999644054 1.0151691591975842 2.870174601960012 0.014533210517364115 25 41 False 1.685676496509504 0.034713965171217304
8 pitch_+0.000_roll_+0.100 0.1 0.0 0.0 8.046465471725153 0.5492256932471735 0.09944242267101946 1.0271293323765827 2.859282573021794 0.0036411815791459468 38 28 False 1.652705907802981 0.034713965171217304
9 pitch_+0.100_roll_-0.200 -0.2 0.1 0.0 7.728092854641688 0.5228446714705329 0.10256719458254226 1.0458160126880083 2.929664876976855 0.07402348553420701 24 42 False 1.7058242505297496 0.034713965171217304
10 pitch_+0.100_roll_-0.100 -0.1 0.1 0.0 7.802880223772661 0.5230373912267472 0.10139401948155134 1.0344448200154543 2.896999001946205 0.04135761050355713 31 35 False 1.6807154082401394 0.03471396517121729
11 pitch_+0.100_roll_+0.100 0.1 0.1 0.0 7.952619015892104 0.5264501536349752 0.09932272291285332 1.0461846579706375 2.885136161262053 0.029494769819405242 30 36 False 1.6476453039285432 0.0347139651712173
12 pitch_+0.200_roll_-0.200 -0.2 0.2 0.0 7.634199171873722 0.49992704876652105 0.10267526429269669 1.0757093910702569 2.9741363007059745 0.11849490926332651 27 39 False 1.7067898021773054 0.034713965171217304
13 pitch_+0.200_roll_-0.100 -0.1 0.2 0.0 7.709023440769963 0.5001285987059726 0.10146524807256484 1.0646575627535269 2.9414386837539155 0.08579729231126754 30 36 False 1.6816953294526065 0.03471396517121731
14 pitch_+0.200_roll_+0.100 0.1 0.2 0.0 7.8588344993060915 0.5036965847149959 0.09931755660454081 1.0760678191977628 2.928699135454269 0.07305774401162113 29 37 False 1.648644705864238 0.034713965171217304
15 pitch_+0.300_roll_-0.200 -0.2 0.3 0.0 7.540367539154748 0.47701910095086286 0.102893833307342 1.1155635852142416 3.035256961724302 0.17961557028165398 25 41 False 1.7135991876939975 0.03471396517121731
16 pitch_+0.300_roll_-0.100 -0.1 0.3 0.0 7.615229271779279 0.47723032409407057 0.10164837804314657 1.1049105360171487 3.0027098217989345 0.14706843035628658 25 41 False 1.6886059172123598 0.0347139651712173
17 pitch_+0.300_roll_+0.100 0.1 0.3 0.0 7.7651136462045685 0.48096817789925783 0.09942680753866891 1.1159092500059349 2.989195517179804 0.13355412573715597 28 38 False 1.6556931398905776 0.034713965171217304
18 yaw_-0.200_diagnostic 0.0 0.0 -0.2 7.9342751812671235 0.5471090190705757 0.10053142254152782 1.0152957563636194 2.8576089058597884 0.0019675144171404924 35 31 False 1.6662748432297787 0.0347139651712173
19 yaw_+0.200_diagnostic 0.0 0.0 0.2 8.012589151848083 0.547109019070576 0.10091495789660097 1.0152957563636194 2.8630112438600364 0.007369852417388412 18 48 False 1.6662748432297787 0.0347139651712173
results/01_previous_two_batches/diagnostics/open3d_refined_pair0_left_rpy_scan.json
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results/01_previous_two_batches/diagnostics/small_gicp_refined_pair0_left_rpy_scan.csv
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label,roll_correction_deg,pitch_correction_deg,yaw_correction_deg,selected_pair_translation_cm,selected_pair_rotation_deg,all_pair_translation_rms_m,all_pair_rotation_rms_deg,normalized_global_rms,normalized_global_rms_change,improved_pairs,worsened_pairs,global_consistency_signal,ground_normal_tilt_rms_deg,ground_height_rms_m
baseline,0.0,0.0,0.0,9.17040157943579,0.3948131792835417,0.11878243181951181,1.1425260172834293,3.296235617224735,0.0,0,0,False,1.6662748432297785,0.03471396517121729
pitch_+0.100,0.0,0.1,0.0,9.076928136371041,0.4171107485761563,0.11876672804528438,1.1650719587800913,3.3274291807303316,0.031193563505596433,37,43,False,1.661255702204205,0.03471396517121729
pitch_+0.200,0.0,0.2,0.0,8.983515248729091,0.4394867360130287,0.11886418170047355,1.2000618469690334,3.378175724075376,0.08194010685064068,33,47,False,1.6622470214176333,0.03471396517121729
pitch_+0.300,0.0,0.3,0.0,8.890164410286015,0.4619296835950138,0.11907438084482405,1.246448116042569,3.4476100262900324,0.1513744090652973,29,51,False,1.669238092496052,0.034713965171217276
pitch_+0.000_roll_-0.200,-0.2,0.0,0.0,9.01972941526644,0.4032977537513619,0.12043024872839399,1.1611526935950138,3.3458153318563415,0.049579714631606375,32,48,False,1.7107124278398285,0.034713965171217304
pitch_+0.000_roll_-0.100,-0.1,0.0,0.0,9.095041148790378,0.3984113441028921,0.1195670451553767,1.1451882592248208,3.3112438616237037,0.015008244398968529,36,44,False,1.685676496509504,0.034713965171217304
pitch_+0.000_roll_+0.100,0.1,0.0,0.0,9.245809148607497,0.39253869750368725,0.1180779565326178,1.153258101553409,3.3010571825949637,0.004821565370228598,39,41,False,1.652705907802981,0.034713965171217304
pitch_+0.100_roll_-0.200,-0.2,0.1,0.0,8.926223519858327,0.4251505609368216,0.12047176074785593,1.1833438511380916,3.3773641697603143,0.08112855253557916,35,45,False,1.7058242505297496,0.034713965171217304
pitch_+0.100_roll_-0.100,-0.1,0.1,0.0,9.001551548682645,0.4205181630564456,0.1195801596045202,1.1676828282839524,3.342708269995132,0.046472652770396916,34,46,False,1.6807154082401394,0.03471396517121729
pitch_+0.100_roll_+0.100,0.1,0.1,0.0,9.152351713867697,0.41495850563128295,0.11803306342745587,1.175598202053685,3.331793234208173,0.035557616983437956,33,47,False,1.6476453039285432,0.0347139651712173
pitch_+0.200_roll_-0.200,-0.2,0.2,0.0,8.832778133209484,0.4471243504044202,0.12062476910644687,1.2178088730560497,3.428172658132847,0.13193704090811176,30,50,False,1.7067898021773054,0.034713965171217304
pitch_+0.200_roll_-0.100,-0.1,0.2,0.0,8.908122483450981,0.44272195299870193,0.11970563817510238,1.2025967761770837,3.3936310861155006,0.09739546889076545,33,47,False,1.6816953294526065,0.03471396517121731
pitch_+0.200_roll_+0.100,0.1,0.2,0.0,9.058954849462527,0.4374446064319599,0.11810204126073977,1.2102837520093128,3.3820679918494174,0.0858323746246823,32,48,False,1.648644705864238,0.034713965171217304
pitch_+0.300_roll_-0.200,-0.2,0.3,0.0,8.739394768298625,0.469202063453847,0.1208887180452031,1.263543756747655,3.4973996284538247,0.20116401122908956,29,51,False,1.7135991876939975,0.03471396517121731
pitch_+0.300_roll_-0.100,-0.1,0.3,0.0,8.81475545648032,0.46500876133584307,0.11994299492844523,1.2488889095688314,3.4631521547194706,0.16691653749473545,30,50,False,1.6886059172123598,0.0347139651712173
pitch_+0.300_roll_+0.100,0.1,0.3,0.0,8.96562003954328,0.45998721539756443,0.11828455597352863,1.2562925755921872,3.4510257337050003,0.1547901164802652,29,51,False,1.6556931398905776,0.034713965171217304
yaw_-0.200_diagnostic,0.0,0.0,-0.2,9.118174501598277,0.39481317928354154,0.11873527555886121,1.142526017283429,3.295555956965929,-0.0006796602588061695,45,35,True,1.6662748432297787,0.0347139651712173
yaw_+0.200_diagnostic,0.0,0.0,0.2,9.225647367504836,0.39481317928354204,0.1194719055604376,1.142526017283429,3.306187727273662,0.009952110048927043,27,53,False,1.6662748432297787,0.0347139651712173
1 label roll_correction_deg pitch_correction_deg yaw_correction_deg selected_pair_translation_cm selected_pair_rotation_deg all_pair_translation_rms_m all_pair_rotation_rms_deg normalized_global_rms normalized_global_rms_change improved_pairs worsened_pairs global_consistency_signal ground_normal_tilt_rms_deg ground_height_rms_m
2 baseline 0.0 0.0 0.0 9.17040157943579 0.3948131792835417 0.11878243181951181 1.1425260172834293 3.296235617224735 0.0 0 0 False 1.6662748432297785 0.03471396517121729
3 pitch_+0.100 0.0 0.1 0.0 9.076928136371041 0.4171107485761563 0.11876672804528438 1.1650719587800913 3.3274291807303316 0.031193563505596433 37 43 False 1.661255702204205 0.03471396517121729
4 pitch_+0.200 0.0 0.2 0.0 8.983515248729091 0.4394867360130287 0.11886418170047355 1.2000618469690334 3.378175724075376 0.08194010685064068 33 47 False 1.6622470214176333 0.03471396517121729
5 pitch_+0.300 0.0 0.3 0.0 8.890164410286015 0.4619296835950138 0.11907438084482405 1.246448116042569 3.4476100262900324 0.1513744090652973 29 51 False 1.669238092496052 0.034713965171217276
6 pitch_+0.000_roll_-0.200 -0.2 0.0 0.0 9.01972941526644 0.4032977537513619 0.12043024872839399 1.1611526935950138 3.3458153318563415 0.049579714631606375 32 48 False 1.7107124278398285 0.034713965171217304
7 pitch_+0.000_roll_-0.100 -0.1 0.0 0.0 9.095041148790378 0.3984113441028921 0.1195670451553767 1.1451882592248208 3.3112438616237037 0.015008244398968529 36 44 False 1.685676496509504 0.034713965171217304
8 pitch_+0.000_roll_+0.100 0.1 0.0 0.0 9.245809148607497 0.39253869750368725 0.1180779565326178 1.153258101553409 3.3010571825949637 0.004821565370228598 39 41 False 1.652705907802981 0.034713965171217304
9 pitch_+0.100_roll_-0.200 -0.2 0.1 0.0 8.926223519858327 0.4251505609368216 0.12047176074785593 1.1833438511380916 3.3773641697603143 0.08112855253557916 35 45 False 1.7058242505297496 0.034713965171217304
10 pitch_+0.100_roll_-0.100 -0.1 0.1 0.0 9.001551548682645 0.4205181630564456 0.1195801596045202 1.1676828282839524 3.342708269995132 0.046472652770396916 34 46 False 1.6807154082401394 0.03471396517121729
11 pitch_+0.100_roll_+0.100 0.1 0.1 0.0 9.152351713867697 0.41495850563128295 0.11803306342745587 1.175598202053685 3.331793234208173 0.035557616983437956 33 47 False 1.6476453039285432 0.0347139651712173
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results/01_previous_two_batches/final_extrinsic_deployment.json
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results/01_previous_two_batches/final_extrinsic_recommended.json
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results/01_previous_two_batches/final_summary.json
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},
"separate_backend_results": {
"open3d_gicp": {
"translation_m": [
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}
},
"warning": "AX rotation RMS remains about one degree; this is not centimetre-grade absolute certification."
}
results/01_previous_two_batches/open3d_gicp/B_batch1_auxiliary.npz
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results/01_previous_two_batches/open3d_gicp/B_batch1_quality.csv
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@@ -1,109 +0,0 @@
i,j,rtk_translation_m,rtk_rotation_deg,heldout_inlier_ratio,heldout_inlier_rmse_m,hessian_rank,hessian_condition,reverse_translation_m,reverse_rotation_deg,multistart_success_rate,accepted,rejection_reasons
0,1,4.069460063439237,24.612641616688823,0.742135248947238,0.1176855237061111,6,4.407651482488117,0.013825313356667012,0.06962701621921227,1.0,True,
0,2,8.883155687691525,14.186041609026283,0.6152139070398225,0.12748244203393638,6,7.377377816318336,0.010848197501521375,0.23936253539883626,1.0,True,
0,3,5.800196618465403,50.885974946353855,0.7200942109830172,0.11742249635115339,6,5.031984522656203,0.010896220625647894,0.1047126090415015,1.0,True,
1,2,4.892046307546544,10.426600007662538,0.7419273778112828,0.11333156226718596,6,4.459403177706152,0.011700301190659394,0.07531112640817904,1.0,True,
1,3,1.7315947839093804,26.273333329665036,0.860897030953885,0.10772639754828932,6,3.19377614050938,0.0031419813068167823,0.07553165598013784,1.0,True,
1,4,1.6946255625760025,60.40345832959281,0.881875,0.10577894988132426,6,3.092963722704653,0.004385743087150129,0.11984963227966941,1.0,True,
2,3,3.2805755806518446,36.69993333732758,0.7933853118712274,0.10942029884827265,6,3.7004807304428255,0.008219359847113026,0.1277049389798412,1.0,True,
2,4,4.866335020594746,70.83005833725535,0.7129525942515864,0.11823277244188567,6,3.5207832348575936,0.003926646926802803,0.12676782091003777,1.0,True,
2,5,1.8274406190287722,30.448549979291712,0.7434186132740082,0.11591916677323456,6,3.2245913426430066,0.005762289421636631,0.0417195018069042,1.0,True,
3,4,1.75881263274858,34.13012499992777,0.8393594395095709,0.11198421236679276,6,2.9955796261301395,0.016040499501420776,0.10475252258183747,1.0,True,
3,5,2.1020200425132134,67.14848331661929,0.7942090219957748,0.1150960059583273,6,3.466731955810642,0.01716401750472473,0.19985666972488197,1.0,True,
3,6,5.791805311102367,61.163322289150074,0.6534504391468005,0.12221285442048085,6,4.065547866736895,0.010758157432153631,0.2541461196933768,1.0,True,
4,5,3.30592832159088,101.27860831654709,0.7519675356615839,0.11239744207499523,6,3.538427973689991,0.007054246334297592,0.24517862996973167,1.0,True,
4,6,6.453995240105232,95.29344728907782,0.6228429546865301,0.12861898753238143,6,5.239918976750594,0.01896562608787198,0.25933537698897335,1.0,True,
4,7,9.537609568572432,26.32130681988008,0.6193556570268899,0.13116150037214716,6,6.94052030016787,0.01746189088684816,0.36780633600082835,1.0,True,
5,6,3.7769167422851373,5.985161027469212,0.7384387717351092,0.11889495868628594,6,3.6057257786167156,0.00882639593332281,0.0737386250122742,1.0,True,
5,7,6.565348405821576,74.95730149666699,0.6792642983119174,0.11864042030155272,6,4.820407924638239,0.009720083307578809,0.20972540690307045,1.0,True,
5,8,6.3343271669452825,107.99940292217995,0.6467507736253273,0.12542317047495377,6,3.0799731272083655,0.01002585992591628,0.05691592874139322,1.0,True,
6,7,3.1558607172566147,68.97214046919777,0.7476148072764279,0.11288592031143596,6,3.5859463118826977,0.010814191328094169,0.0266481566439959,1.0,True,
6,8,3.3979014114419868,102.01424189471071,0.6804804804804805,0.11508385505331041,6,2.2706877166387276,0.004482804469688638,0.1044466289150781,1.0,True,
6,9,0.28270347875173524,124.11602215258995,0.7371697154471545,0.11682527448508172,6,2.8975705145594723,0.005203408768588573,0.04849411501759064,1.0,True,
7,8,4.78580085090425,33.04210142551296,0.664336521952416,0.11766047279270792,6,2.929070995599858,0.0018478739597912201,0.09974755915747875,1.0,True,
7,9,2.876695741106431,166.91183737821345,0.6077132839890923,0.1180552047396233,6,3.7184930305903685,0.010919730241803961,0.14576304064343415,1.0,True,
7,10,3.5227760858025783,151.5297683494061,0.8481789137380191,0.10615102438506704,6,2.8662462784517695,0.006788694147854834,0.052872203183928275,1.0,True,
8,9,3.469611483011297,133.86973595269956,0.5943661971830986,0.11920565901187036,6,3.7883774947006965,0.0042443197269814905,0.10102914613043958,1.0,True,
8,10,8.226583602288494,175.42813022508852,0.5924719507781397,0.12046539737502422,6,6.6212639471399966,0.012068925048483609,0.06971570917645852,1.0,True,
8,11,1.865267488778983,173.7180803410843,0.6789593030194534,0.1124672015619688,6,2.333957745729889,0.006563181150688944,0.09301162138898374,1.0,True,
9,10,5.488028285806731,41.558394272381754,0.6015824400204186,0.11914584090399423,6,5.490861355062907,0.023677817470035802,0.12091279277864625,1.0,True,
9,11,4.718056434111957,39.84834438838189,0.5936159475145092,0.12220038333634799,6,2.975477564952316,0.010739868846460122,0.11267510434066752,1.0,True,
9,12,4.0664516687652705,11.300244475855449,0.6125753012048193,0.12248054470736866,6,2.850813791288553,0.006578056867618694,0.0648765356282168,1.0,True,
10,11,8.48963239412477,1.7100498839998592,0.5922981366459628,0.1252332170275593,6,3.561970732702301,0.0101884878590875,0.04905586883665525,1.0,True,
10,12,7.8510898708654135,30.258149796526293,0.5541275333662877,0.12481360204475032,6,3.7517795436266956,0.031261518480906504,0.2822575477830575,1.0,True,
10,13,7.303425556850936,93.89697482620899,0.4898572501878287,0.1234709130125476,6,5.011926866876523,0.002887507644989318,0.12861291537137956,1.0,True,
11,12,0.684356309096518,28.54809991252644,0.8033488144707895,0.10033649821782255,6,2.8495161791494152,0.0037957676909615035,0.030493386938618566,1.0,True,
11,13,4.983180422136547,92.18692494220912,0.48959881129271915,0.13002768142853224,6,7.500744983348576,0.04820000634069387,0.21663658694591276,1.0,True,
11,14,7.513058208926106,116.17598776756182,0.5511551155115512,0.1363490361205856,6,8.69179713026475,0.020420243910966678,0.3388561639372597,1.0,True,
12,13,4.470221622128888,63.638825029682685,0.5484109386548411,0.12959344453923985,6,6.303133528073185,0.011857268436770598,0.07665557322523474,1.0,True,
12,14,7.042432348069883,87.62788785503537,0.5823908549191292,0.13141832933236727,6,7.376965777627224,0.010833935971029728,0.22584716035991784,1.0,True,
12,15,9.425648786798952,64.11974003309916,0.5143102812922246,0.13728792513153318,6,10.6657111200586,0.012453922151317217,0.21281971313986298,1.0,True,
13,14,2.6059162757855927,23.989062825352686,0.6649008254281138,0.11829356218038511,6,4.46953470312236,0.010710695211339957,0.05103080008067046,1.0,True,
13,15,4.985522367267706,0.4809150034164723,0.6433105164903546,0.12280456147298241,6,5.48212715248282,0.002079348825514708,0.08095419399489742,1.0,True,
13,16,5.330126890587504,0.570225046299944,0.5092755076460266,0.12767535698436475,6,5.1227851245513,0.01771530501608281,0.1987385247368959,1.0,True,
14,15,2.3845573265941185,23.508147821936216,0.7934889434889435,0.10996554201681306,6,3.5978390564938083,0.010537581931095797,0.18886023198851049,1.0,True,
14,16,7.871327660405588,24.55928787165262,0.5124335024124707,0.1271130929550258,6,6.773046277884419,0.0367675981786339,0.17067950171934768,1.0,True,
14,17,6.334147330965719,0.13339551798522414,0.5997034840622684,0.12123178848040567,6,5.0521521307734485,0.007039639082169618,0.1420706023439064,1.0,True,
15,16,10.188241681839806,1.051140049716416,0.4525684289463817,0.1310762605599437,6,10.161233588287896,0.03028489261617412,0.2306307424740811,1.0,True,
15,17,8.639041695194567,23.641543339921444,0.5419370943584623,0.12926666329467473,6,7.1208904116826295,0.016749087599431237,0.21516518274709787,1.0,True,
15,18,9.620687246763701,76.25281000098852,0.5239486723415068,0.13468232645821807,6,7.765728655204814,0.015824236097669597,0.5418501890608881,1.0,False,forward_reverse_rotation
16,17,1.5531467972267479,24.69268338963786,0.725455688246386,0.11626130134637655,6,2.5539312507809044,0.008173066808338859,0.1459219364983215,1.0,True,
16,18,0.5677742310084086,77.30395005070493,0.7509598157153826,0.11215936550371307,6,2.72958456426664,0.01007065253007885,0.024451533638690782,1.0,True,
16,19,2.9811390265794215,109.91770005034847,0.61725,0.11278472852394253,6,3.8445921585562353,0.013911959588582991,0.09838261866068182,1.0,True,
17,18,0.98603936348044,52.61126666106708,0.7936102236421725,0.10947670477666799,6,2.9613430589728713,0.004348617987976676,0.08578297576648478,1.0,True,
17,19,3.326868398717413,85.22501666071064,0.6638372238172513,0.11457050422944211,6,4.039052271877423,0.011384309901198326,0.08601325248059774,1.0,True,
17,20,1.702964560097507,146.95247656520317,0.6755903523172118,0.11461754659819628,6,2.9771145257220444,0.003070224389531284,0.041777705584758074,1.0,True,
18,19,3.0423123591285863,32.61374999964355,0.7679796696315121,0.10951225500902462,6,3.162387301290471,0.0016757410207260358,0.05240557176555545,1.0,True,
18,20,1.0853574230860805,94.34120990413592,0.7909488300334276,0.11336174254244849,6,2.472783746978993,0.0011490974177271606,0.05798276029492007,1.0,True,
18,21,0.7190584934612743,134.43032499953063,0.7659654868371332,0.11623488568922577,6,2.3334272634442037,0.0007397588392132655,0.049540488476333674,1.0,True,
19,20,1.9962827272376449,61.72745990449234,0.7140883977900553,0.1080327967474786,6,2.8867642059111027,0.008102122424322853,0.0646114560547033,1.0,True,
19,21,3.133843614009462,101.81657499988698,0.6584615384615384,0.10963878686899117,6,2.8691912248684632,0.012913229197399395,0.08080164801156113,1.0,True,
19,22,3.558853688535236,131.87013333415302,0.6261591020009761,0.11484815169172223,6,3.1506020281084415,0.008079663853152794,0.06664730348633674,1.0,True,
20,21,1.4312693216245356,40.08911509539463,0.8130212871903398,0.10717601941116739,6,2.598682401671686,0.003986207075465725,0.014468856174509968,1.0,True,
20,22,2.8745434122269677,70.1426734296607,0.709279368213228,0.11654592484503022,6,3.3550200552976293,0.004732316748497569,0.04653511032479733,1.0,True,
20,23,2.368927629183393,88.7954400941961,0.7151155604993202,0.11440209684434971,6,3.0862044513861178,0.013864191008669256,0.04741774806449891,1.0,True,
21,22,1.9888588003695142,30.053558334266054,0.7434966727162734,0.11011755646689264,6,3.014221192692681,0.0010075959724342985,0.03301381213076696,1.0,True,
21,23,1.9477165743251885,48.70632499880147,0.7529083858458555,0.11149633235838309,6,3.077926485095861,0.001989351505445817,0.010666288445208057,1.0,True,
21,24,3.76075382790461,86.89547500633407,0.7106949236076885,0.11526418670282225,6,3.884216729530811,0.0033123302523526757,0.010450448797924646,1.0,True,
22,23,0.8778876913955667,18.652766664535406,0.8800913132284032,0.0910569893232571,6,2.4100478670835583,0.0032245216994413483,0.016462830433921317,1.0,True,
22,24,2.2510479983846268,56.84191667206801,0.746915842188836,0.11145740682737049,6,2.851886485949151,0.01259476909551429,0.07575320422516746,1.0,True,
22,25,3.452614527841686,105.36038662410485,0.6202953787517865,0.10996904036547328,6,3.4469213215286616,0.01971125516503169,0.10319789629151777,1.0,True,
23,24,1.8212773036309853,38.18915000753262,0.7859065329092244,0.11467536863831725,6,2.8422525641977647,0.011606021246419816,0.08140725284903579,1.0,True,
23,25,2.999856552735995,86.70761995956946,0.6364070141953955,0.11293944470788622,6,3.2389862166172447,0.02314438992859391,0.07068788850704667,1.0,True,
23,26,1.7088514993141615,157.34900000166795,0.6150898203592814,0.11134349524292353,6,3.3995614886239296,0.039322949699257106,0.11562859307450143,1.0,True,
24,25,1.2059352225587747,48.51846995203683,0.7858615254031769,0.11149776506996652,6,2.476851261276463,0.011240124367435099,0.1049569848741807,1.0,True,
24,26,1.1958827550030364,119.15984999413537,0.7456781105429754,0.10341842736085993,6,2.5300194557440525,0.01395883182919579,0.08780591339848752,1.0,True,
24,27,3.786115539231191,149.59794999698258,0.621380846325167,0.10953325191126222,6,3.7363642522268807,0.016637332395867215,0.10353943868518889,1.0,True,
25,26,1.8270495459501794,70.64138004209858,0.8347382167873679,0.10115747661888057,6,3.0333095788198037,0.01268722454395532,0.041403863092781025,1.0,True,
25,27,4.741885618180033,101.07948004494568,0.624412013026173,0.11277808045896366,6,3.9078457957611743,0.025589655197266314,0.15422514597254583,1.0,True,
25,28,3.8640843382258447,141.59343004213218,0.5869307400379506,0.11770827672499123,6,4.799123781945888,0.022027669812843296,0.2897817423195282,1.0,True,
26,27,2.939444415707082,30.43810000284707,0.6580285783482683,0.1098010629341849,6,3.5110471043097586,0.0028795138573569803,0.007524827744647535,1.0,True,
26,28,2.285770454773634,70.95205000003368,0.6673413501607334,0.11114983317987888,6,3.8873517833668108,0.008151782060846088,0.22733511766651973,1.0,True,
26,29,3.6568094249627374,131.92299999999997,0.567654409139593,0.1129321723600836,6,3.929185583738771,0.018525590401292088,0.6411617076853331,1.0,False,forward_reverse_rotation
27,28,2.3764785758716087,40.513949997186636,0.795572759162937,0.10258057277674534,6,3.138921602248635,0.0021193681800924964,0.11642323746608753,1.0,True,
27,29,4.061689152213973,101.48489999715295,0.7440454600411075,0.10506613395932038,6,3.1227510205159508,0.010617830050775887,0.010252586949293494,1.0,True,
27,30,5.033742765789926,178.61309998505533,0.5825184112036702,0.11453665822491028,6,7.650962341345717,2.5431128502617377,2.5742599673919253,1.0,False,forward_reverse_translation;forward_reverse_rotation
28,29,1.7860052935263149,60.970949999966315,0.829172610556348,0.101007907265047,6,3.8068503338763042,0.002050657211153992,0.060432422726630436,1.0,True,
28,30,7.378510482049102,138.09914998778677,0.5397673314339981,0.12193285407050827,6,9.554349687793176,2.3478431587463846,1.5786016001583607,1.0,False,forward_reverse_translation;forward_reverse_rotation
28,31,8.396001985859645,161.15751666826222,0.5045164398410213,0.12642928247376978,6,9.31764609998303,0.020394024193417442,0.4096021180283358,1.0,True,
29,30,8.925911734961222,77.12819998782052,0.5217133364973896,0.12674065647229513,6,8.522188842167411,0.029506475723043876,0.11389243918851435,1.0,True,
29,31,9.936139535050964,100.18656666829587,0.49500421331407246,0.12930714939449198,6,8.914982386428042,0.09268316078142827,0.5769686714189283,1.0,False,forward_reverse_translation;forward_reverse_rotation
29,32,9.719184595429152,159.1482027457047,0.4949034656433625,0.12826034683961704,6,9.48481031343127,0.046228293643484374,0.3497292020413176,0.0,False,multistart_instability
30,31,1.0176267260390144,23.058366680475352,0.8362783988460152,0.09839553390448733,6,3.4866604582833345,0.00292801654357991,0.07029435398214784,1.0,True,
30,32,0.8427696971904044,82.02000275788396,0.8588193030774758,0.09922846236206209,6,3.665055653691297,0.007868521232743847,0.0646663588791515,1.0,True,
30,33,1.1320092061720382,147.1799498371321,0.7985030295829868,0.09950089003213429,6,4.012416511656465,0.002818384349407474,0.04751878128625789,1.0,True,
31,32,0.2771314063233405,58.9616360774086,0.8562575941676792,0.09641015722415121,6,3.1475922536183365,0.00743603397476557,0.05749362415769439,1.0,True,
31,33,0.5409306841643147,124.12158315665668,0.7594021215043394,0.09971190839470828,6,3.924941555652805,0.008658288176145966,0.04699249624411687,1.0,True,
31,34,0.9901318542574941,155.5976733552555,0.7827868852459017,0.09387821156002771,6,3.677769944550314,0.001760438169327793,0.07141137968742438,0.0,False,multistart_instability
32,33,0.38765596924480794,65.15994707924807,0.859390009606148,0.09596734045987164,6,3.3457711754162425,0.007599187780672587,0.08086834965344242,1.0,True,
32,34,1.0327853226589305,96.63603727784675,0.8306916426512968,0.09663160733562838,6,3.561692133355112,0.008708468398558678,0.031578495955716095,1.0,True,
32,35,1.1947451269258382,131.6892972449946,0.8055028462998103,0.09329597448562056,6,3.202004817860687,0.002684669665422271,0.0241000049832631,1.0,True,
33,34,0.7453275493729801,31.47609019859864,0.8550154872527996,0.09764473733600805,6,3.153108280248987,0.005560284067502743,0.07666131775182662,1.0,True,
33,35,1.0924186931915323,66.52935016574651,0.8344316309719935,0.08989959394950665,6,3.014281766333208,0.0013737288250339052,0.028590963377823297,1.0,True,
33,36,3.335020341384374,123.21987515531416,0.7655961609449982,0.10046425727734096,6,3.222688396070449,0.012544124903927084,0.05831564134554671,1.0,True,
34,35,0.6186130964444055,35.05325996714787,0.8692633560837845,0.08711586696140278,6,2.9228018947680083,0.006834102546614079,0.04740606842462777,1.0,True,
34,36,3.114273556969299,91.74378495671547,0.771256306140027,0.09836949441930562,6,3.7389528980898077,0.004832468349243269,0.046439990812924645,1.0,True,
34,37,2.8155528331227235,126.08390993626574,0.722881252293017,0.10001696647063055,6,3.8298681993003196,0.0035748664819913244,0.09211471145637762,1.0,True,
35,36,2.4989423629184655,56.69052498956759,0.7697728101081278,0.09462474495647087,6,3.9667933176330195,0.004833519542267701,0.07989067847153554,1.0,True,
35,37,2.2182956249874577,91.03064996911786,0.7351769110010868,0.09626480689856566,6,3.604883740945235,0.00826951342075784,0.05772015666601106,1.0,True,
36,37,0.5129211474320938,34.34012497955026,0.8790139064475347,0.08686306972783016,6,2.86419132160936,0.0018743847744752261,0.0426177033120738,1.0,True,
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 4.069460063439237 24.612641616688823 0.742135248947238 0.1176855237061111 6 4.407651482488117 0.013825313356667012 0.06962701621921227 1.0 True
3 0 2 8.883155687691525 14.186041609026283 0.6152139070398225 0.12748244203393638 6 7.377377816318336 0.010848197501521375 0.23936253539883626 1.0 True
4 0 3 5.800196618465403 50.885974946353855 0.7200942109830172 0.11742249635115339 6 5.031984522656203 0.010896220625647894 0.1047126090415015 1.0 True
5 1 2 4.892046307546544 10.426600007662538 0.7419273778112828 0.11333156226718596 6 4.459403177706152 0.011700301190659394 0.07531112640817904 1.0 True
6 1 3 1.7315947839093804 26.273333329665036 0.860897030953885 0.10772639754828932 6 3.19377614050938 0.0031419813068167823 0.07553165598013784 1.0 True
7 1 4 1.6946255625760025 60.40345832959281 0.881875 0.10577894988132426 6 3.092963722704653 0.004385743087150129 0.11984963227966941 1.0 True
8 2 3 3.2805755806518446 36.69993333732758 0.7933853118712274 0.10942029884827265 6 3.7004807304428255 0.008219359847113026 0.1277049389798412 1.0 True
9 2 4 4.866335020594746 70.83005833725535 0.7129525942515864 0.11823277244188567 6 3.5207832348575936 0.003926646926802803 0.12676782091003777 1.0 True
10 2 5 1.8274406190287722 30.448549979291712 0.7434186132740082 0.11591916677323456 6 3.2245913426430066 0.005762289421636631 0.0417195018069042 1.0 True
11 3 4 1.75881263274858 34.13012499992777 0.8393594395095709 0.11198421236679276 6 2.9955796261301395 0.016040499501420776 0.10475252258183747 1.0 True
12 3 5 2.1020200425132134 67.14848331661929 0.7942090219957748 0.1150960059583273 6 3.466731955810642 0.01716401750472473 0.19985666972488197 1.0 True
13 3 6 5.791805311102367 61.163322289150074 0.6534504391468005 0.12221285442048085 6 4.065547866736895 0.010758157432153631 0.2541461196933768 1.0 True
14 4 5 3.30592832159088 101.27860831654709 0.7519675356615839 0.11239744207499523 6 3.538427973689991 0.007054246334297592 0.24517862996973167 1.0 True
15 4 6 6.453995240105232 95.29344728907782 0.6228429546865301 0.12861898753238143 6 5.239918976750594 0.01896562608787198 0.25933537698897335 1.0 True
16 4 7 9.537609568572432 26.32130681988008 0.6193556570268899 0.13116150037214716 6 6.94052030016787 0.01746189088684816 0.36780633600082835 1.0 True
17 5 6 3.7769167422851373 5.985161027469212 0.7384387717351092 0.11889495868628594 6 3.6057257786167156 0.00882639593332281 0.0737386250122742 1.0 True
18 5 7 6.565348405821576 74.95730149666699 0.6792642983119174 0.11864042030155272 6 4.820407924638239 0.009720083307578809 0.20972540690307045 1.0 True
19 5 8 6.3343271669452825 107.99940292217995 0.6467507736253273 0.12542317047495377 6 3.0799731272083655 0.01002585992591628 0.05691592874139322 1.0 True
20 6 7 3.1558607172566147 68.97214046919777 0.7476148072764279 0.11288592031143596 6 3.5859463118826977 0.010814191328094169 0.0266481566439959 1.0 True
21 6 8 3.3979014114419868 102.01424189471071 0.6804804804804805 0.11508385505331041 6 2.2706877166387276 0.004482804469688638 0.1044466289150781 1.0 True
22 6 9 0.28270347875173524 124.11602215258995 0.7371697154471545 0.11682527448508172 6 2.8975705145594723 0.005203408768588573 0.04849411501759064 1.0 True
23 7 8 4.78580085090425 33.04210142551296 0.664336521952416 0.11766047279270792 6 2.929070995599858 0.0018478739597912201 0.09974755915747875 1.0 True
24 7 9 2.876695741106431 166.91183737821345 0.6077132839890923 0.1180552047396233 6 3.7184930305903685 0.010919730241803961 0.14576304064343415 1.0 True
25 7 10 3.5227760858025783 151.5297683494061 0.8481789137380191 0.10615102438506704 6 2.8662462784517695 0.006788694147854834 0.052872203183928275 1.0 True
26 8 9 3.469611483011297 133.86973595269956 0.5943661971830986 0.11920565901187036 6 3.7883774947006965 0.0042443197269814905 0.10102914613043958 1.0 True
27 8 10 8.226583602288494 175.42813022508852 0.5924719507781397 0.12046539737502422 6 6.6212639471399966 0.012068925048483609 0.06971570917645852 1.0 True
28 8 11 1.865267488778983 173.7180803410843 0.6789593030194534 0.1124672015619688 6 2.333957745729889 0.006563181150688944 0.09301162138898374 1.0 True
29 9 10 5.488028285806731 41.558394272381754 0.6015824400204186 0.11914584090399423 6 5.490861355062907 0.023677817470035802 0.12091279277864625 1.0 True
30 9 11 4.718056434111957 39.84834438838189 0.5936159475145092 0.12220038333634799 6 2.975477564952316 0.010739868846460122 0.11267510434066752 1.0 True
31 9 12 4.0664516687652705 11.300244475855449 0.6125753012048193 0.12248054470736866 6 2.850813791288553 0.006578056867618694 0.0648765356282168 1.0 True
32 10 11 8.48963239412477 1.7100498839998592 0.5922981366459628 0.1252332170275593 6 3.561970732702301 0.0101884878590875 0.04905586883665525 1.0 True
33 10 12 7.8510898708654135 30.258149796526293 0.5541275333662877 0.12481360204475032 6 3.7517795436266956 0.031261518480906504 0.2822575477830575 1.0 True
34 10 13 7.303425556850936 93.89697482620899 0.4898572501878287 0.1234709130125476 6 5.011926866876523 0.002887507644989318 0.12861291537137956 1.0 True
35 11 12 0.684356309096518 28.54809991252644 0.8033488144707895 0.10033649821782255 6 2.8495161791494152 0.0037957676909615035 0.030493386938618566 1.0 True
36 11 13 4.983180422136547 92.18692494220912 0.48959881129271915 0.13002768142853224 6 7.500744983348576 0.04820000634069387 0.21663658694591276 1.0 True
37 11 14 7.513058208926106 116.17598776756182 0.5511551155115512 0.1363490361205856 6 8.69179713026475 0.020420243910966678 0.3388561639372597 1.0 True
38 12 13 4.470221622128888 63.638825029682685 0.5484109386548411 0.12959344453923985 6 6.303133528073185 0.011857268436770598 0.07665557322523474 1.0 True
39 12 14 7.042432348069883 87.62788785503537 0.5823908549191292 0.13141832933236727 6 7.376965777627224 0.010833935971029728 0.22584716035991784 1.0 True
40 12 15 9.425648786798952 64.11974003309916 0.5143102812922246 0.13728792513153318 6 10.6657111200586 0.012453922151317217 0.21281971313986298 1.0 True
41 13 14 2.6059162757855927 23.989062825352686 0.6649008254281138 0.11829356218038511 6 4.46953470312236 0.010710695211339957 0.05103080008067046 1.0 True
42 13 15 4.985522367267706 0.4809150034164723 0.6433105164903546 0.12280456147298241 6 5.48212715248282 0.002079348825514708 0.08095419399489742 1.0 True
43 13 16 5.330126890587504 0.570225046299944 0.5092755076460266 0.12767535698436475 6 5.1227851245513 0.01771530501608281 0.1987385247368959 1.0 True
44 14 15 2.3845573265941185 23.508147821936216 0.7934889434889435 0.10996554201681306 6 3.5978390564938083 0.010537581931095797 0.18886023198851049 1.0 True
45 14 16 7.871327660405588 24.55928787165262 0.5124335024124707 0.1271130929550258 6 6.773046277884419 0.0367675981786339 0.17067950171934768 1.0 True
46 14 17 6.334147330965719 0.13339551798522414 0.5997034840622684 0.12123178848040567 6 5.0521521307734485 0.007039639082169618 0.1420706023439064 1.0 True
47 15 16 10.188241681839806 1.051140049716416 0.4525684289463817 0.1310762605599437 6 10.161233588287896 0.03028489261617412 0.2306307424740811 1.0 True
48 15 17 8.639041695194567 23.641543339921444 0.5419370943584623 0.12926666329467473 6 7.1208904116826295 0.016749087599431237 0.21516518274709787 1.0 True
49 15 18 9.620687246763701 76.25281000098852 0.5239486723415068 0.13468232645821807 6 7.765728655204814 0.015824236097669597 0.5418501890608881 1.0 False forward_reverse_rotation
50 16 17 1.5531467972267479 24.69268338963786 0.725455688246386 0.11626130134637655 6 2.5539312507809044 0.008173066808338859 0.1459219364983215 1.0 True
51 16 18 0.5677742310084086 77.30395005070493 0.7509598157153826 0.11215936550371307 6 2.72958456426664 0.01007065253007885 0.024451533638690782 1.0 True
52 16 19 2.9811390265794215 109.91770005034847 0.61725 0.11278472852394253 6 3.8445921585562353 0.013911959588582991 0.09838261866068182 1.0 True
53 17 18 0.98603936348044 52.61126666106708 0.7936102236421725 0.10947670477666799 6 2.9613430589728713 0.004348617987976676 0.08578297576648478 1.0 True
54 17 19 3.326868398717413 85.22501666071064 0.6638372238172513 0.11457050422944211 6 4.039052271877423 0.011384309901198326 0.08601325248059774 1.0 True
55 17 20 1.702964560097507 146.95247656520317 0.6755903523172118 0.11461754659819628 6 2.9771145257220444 0.003070224389531284 0.041777705584758074 1.0 True
56 18 19 3.0423123591285863 32.61374999964355 0.7679796696315121 0.10951225500902462 6 3.162387301290471 0.0016757410207260358 0.05240557176555545 1.0 True
57 18 20 1.0853574230860805 94.34120990413592 0.7909488300334276 0.11336174254244849 6 2.472783746978993 0.0011490974177271606 0.05798276029492007 1.0 True
58 18 21 0.7190584934612743 134.43032499953063 0.7659654868371332 0.11623488568922577 6 2.3334272634442037 0.0007397588392132655 0.049540488476333674 1.0 True
59 19 20 1.9962827272376449 61.72745990449234 0.7140883977900553 0.1080327967474786 6 2.8867642059111027 0.008102122424322853 0.0646114560547033 1.0 True
60 19 21 3.133843614009462 101.81657499988698 0.6584615384615384 0.10963878686899117 6 2.8691912248684632 0.012913229197399395 0.08080164801156113 1.0 True
61 19 22 3.558853688535236 131.87013333415302 0.6261591020009761 0.11484815169172223 6 3.1506020281084415 0.008079663853152794 0.06664730348633674 1.0 True
62 20 21 1.4312693216245356 40.08911509539463 0.8130212871903398 0.10717601941116739 6 2.598682401671686 0.003986207075465725 0.014468856174509968 1.0 True
63 20 22 2.8745434122269677 70.1426734296607 0.709279368213228 0.11654592484503022 6 3.3550200552976293 0.004732316748497569 0.04653511032479733 1.0 True
64 20 23 2.368927629183393 88.7954400941961 0.7151155604993202 0.11440209684434971 6 3.0862044513861178 0.013864191008669256 0.04741774806449891 1.0 True
65 21 22 1.9888588003695142 30.053558334266054 0.7434966727162734 0.11011755646689264 6 3.014221192692681 0.0010075959724342985 0.03301381213076696 1.0 True
66 21 23 1.9477165743251885 48.70632499880147 0.7529083858458555 0.11149633235838309 6 3.077926485095861 0.001989351505445817 0.010666288445208057 1.0 True
67 21 24 3.76075382790461 86.89547500633407 0.7106949236076885 0.11526418670282225 6 3.884216729530811 0.0033123302523526757 0.010450448797924646 1.0 True
68 22 23 0.8778876913955667 18.652766664535406 0.8800913132284032 0.0910569893232571 6 2.4100478670835583 0.0032245216994413483 0.016462830433921317 1.0 True
69 22 24 2.2510479983846268 56.84191667206801 0.746915842188836 0.11145740682737049 6 2.851886485949151 0.01259476909551429 0.07575320422516746 1.0 True
70 22 25 3.452614527841686 105.36038662410485 0.6202953787517865 0.10996904036547328 6 3.4469213215286616 0.01971125516503169 0.10319789629151777 1.0 True
71 23 24 1.8212773036309853 38.18915000753262 0.7859065329092244 0.11467536863831725 6 2.8422525641977647 0.011606021246419816 0.08140725284903579 1.0 True
72 23 25 2.999856552735995 86.70761995956946 0.6364070141953955 0.11293944470788622 6 3.2389862166172447 0.02314438992859391 0.07068788850704667 1.0 True
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88 28 31 8.396001985859645 161.15751666826222 0.5045164398410213 0.12642928247376978 6 9.31764609998303 0.020394024193417442 0.4096021180283358 1.0 True
89 29 30 8.925911734961222 77.12819998782052 0.5217133364973896 0.12674065647229513 6 8.522188842167411 0.029506475723043876 0.11389243918851435 1.0 True
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98 32 33 0.38765596924480794 65.15994707924807 0.859390009606148 0.09596734045987164 6 3.3457711754162425 0.007599187780672587 0.08086834965344242 1.0 True
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101 33 34 0.7453275493729801 31.47609019859864 0.8550154872527996 0.09764473733600805 6 3.153108280248987 0.005560284067502743 0.07666131775182662 1.0 True
102 33 35 1.0924186931915323 66.52935016574651 0.8344316309719935 0.08989959394950665 6 3.014281766333208 0.0013737288250339052 0.028590963377823297 1.0 True
103 33 36 3.335020341384374 123.21987515531416 0.7655961609449982 0.10046425727734096 6 3.222688396070449 0.012544124903927084 0.05831564134554671 1.0 True
104 34 35 0.6186130964444055 35.05325996714787 0.8692633560837845 0.08711586696140278 6 2.9228018947680083 0.006834102546614079 0.04740606842462777 1.0 True
105 34 36 3.114273556969299 91.74378495671547 0.771256306140027 0.09836949441930562 6 3.7389528980898077 0.004832468349243269 0.046439990812924645 1.0 True
106 34 37 2.8155528331227235 126.08390993626574 0.722881252293017 0.10001696647063055 6 3.8298681993003196 0.0035748664819913244 0.09211471145637762 1.0 True
107 35 36 2.4989423629184655 56.69052498956759 0.7697728101081278 0.09462474495647087 6 3.9667933176330195 0.004833519542267701 0.07989067847153554 1.0 True
108 35 37 2.2182956249874577 91.03064996911786 0.7351769110010868 0.09626480689856566 6 3.604883740945235 0.00826951342075784 0.05772015666601106 1.0 True
109 36 37 0.5129211474320938 34.34012497955026 0.8790139064475347 0.08686306972783016 6 2.86419132160936 0.0018743847744752261 0.0426177033120738 1.0 True
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i,j,rtk_translation_m,rtk_rotation_deg,heldout_inlier_ratio,heldout_inlier_rmse_m,hessian_rank,hessian_condition,reverse_translation_m,reverse_rotation_deg,multistart_success_rate,accepted,rejection_reasons
0,1,0.503306788400079,13.198324485965863,0.7947725072604066,0.09714510386272111,6,3.1645559924308744,0.00040681040655899406,0.015836974306118006,1.0,True,
0,2,1.1601020961002007,26.306295080591493,0.8196783588704571,0.10111548255452249,6,3.1496351272437915,0.0024786662018318395,0.04305117802148083,1.0,True,
0,3,1.1328421148283978,41.74134818989305,0.8170202208162759,0.10132889132333661,6,2.9636847958606816,0.001763337668740155,0.0152261810177797,1.0,True,
1,2,0.6927979865343085,13.107970594625625,0.7847728726807421,0.08513837010948266,6,2.6465075635845983,0.003621881376895638,0.011153493139183525,1.0,True,
1,3,0.7002295454253883,28.543023703927183,0.7885699962401304,0.09713251502799032,6,2.6341764735488145,0.0014094469663425105,0.02902143941632062,1.0,True,
1,4,0.8045641659338967,57.731023816949346,0.7363658899355479,0.09863727794945644,6,2.7377680197189993,0.011592408190980286,0.17084480097436408,1.0,True,
2,3,0.13949776780243606,15.435053109301553,0.918286915396742,0.07288259780013887,6,2.6383381348446657,0.002273707715582919,0.0026923349652342365,1.0,True,
2,4,0.6701726006967371,44.62305322232371,0.8370341514089079,0.09441127521663696,6,3.0939727970407334,0.0041401597740440875,0.03075174550917053,1.0,True,
2,5,0.8004226998626774,62.96656300910673,0.8235371293623721,0.10626795357026747,6,2.8027866429960917,0.009148796674106823,0.05256412309062332,1.0,True,
3,4,0.5366157827594368,29.188000113022152,0.8850603941513032,0.07613142655207121,6,2.6417590251999084,0.0013904645484531576,0.0259404694028417,1.0,True,
3,5,0.6760093701980526,47.53150989980517,0.8740046236835346,0.09072202522560696,6,2.686157540984516,0.006509933107990933,0.02545212192512678,1.0,True,
3,6,1.4361364806393455,80.09091141471853,0.6795928884308168,0.10271649349323747,6,3.008166890660092,0.00828154435242913,0.026486510290724834,1.0,True,
4,5,0.17861948949299924,18.343509786783002,0.8684444444444445,0.0784387700526441,6,2.6861954442048104,0.0025072579888539386,0.021042298559759365,1.0,True,
4,6,1.5807073315069207,50.90291130169636,0.6781301745000636,0.10313081772493418,6,2.8455217243212387,0.003271195715147848,0.14716032493381534,1.0,True,
4,7,1.7804201387535592,75.18775779751417,0.7873548387096774,0.09891997778736471,6,3.1802938248442474,0.004633577062239209,0.04472721629193465,1.0,True,
5,6,1.5316987521992935,32.55940151491337,0.6911991765311374,0.10152400788085783,6,3.2393513551418907,0.005288907067525738,0.05494235971756111,1.0,True,
5,7,1.6091514152462776,56.844248010731185,0.7747360187719984,0.09121609616459708,6,3.123473300580222,0.00421464356720393,0.015634168997181876,1.0,True,
5,8,1.1872485760158462,85.67585439000777,0.7829477514946712,0.10577559680144696,6,3.109219016133088,0.0033105282814613057,0.030813019102438145,1.0,True,
6,7,1.5692385974903325,24.284846495817817,0.7347979599843075,0.10196810239533251,6,3.458846242963224,0.004020707752206965,0.039850323489805044,1.0,True,
6,8,0.5035712140385419,53.11645287509441,0.7155149934810952,0.10460245111398395,6,3.0180670962260874,0.0072368624874882504,0.0055907688631446776,1.0,True,
6,9,0.8508108569116883,66.44324365209327,0.6825438366919655,0.10495957402394829,6,3.140616320789838,0.00430894236478819,0.13911475434755866,1.0,True,
7,8,1.1230406357307108,28.831606379276582,0.8127890077949531,0.09942756972076687,6,2.9436564519387325,0.008316114154115147,0.01814737917470093,1.0,True,
7,9,0.7186795671679719,42.158397156275456,0.8193873491579367,0.09507291602212191,6,3.5012345051117295,0.006751593996528964,0.029293578235221863,1.0,True,
7,10,0.39293571356254015,58.25702665650087,0.7940216681895313,0.10356080767641777,6,2.817840996766873,0.006883567504360684,0.016492683422577963,1.0,True,
8,9,0.4407680716864947,13.326790776998866,0.8581438392384981,0.09484304022144129,6,3.219825075793889,0.007253035671159373,0.024252719250072975,1.0,True,
8,10,0.968166048306331,29.425420277224287,0.8015356585111921,0.10152298131640612,6,3.7446585062478155,0.0017523919908150464,0.05042176209078739,1.0,True,
8,11,3.0540124804140354,66.34511043540417,0.688687555052221,0.10536230082368817,6,4.696625415382456,0.0036281146181207165,0.013078005975049522,1.0,True,
9,10,0.5276649044820116,16.098629500225417,0.8560606060606061,0.09594368034796862,6,3.2778548903058375,0.010491037599418197,0.0599937188173442,1.0,True,
9,11,2.680593454060616,53.0183196584053,0.6966788735951509,0.10384409271666459,6,4.3419055531595605,0.009543265092514783,0.09977883713501384,1.0,True,
9,12,3.2944321799753977,73.22024906015783,0.6579243162233046,0.10621203405076814,6,4.293885241325807,0.0028782078295197843,0.0743495992598884,1.0,True,
10,11,2.249454986440387,36.91969015817989,0.729195173529046,0.10869447817964137,6,4.322382098282597,0.0028206125960216254,0.014140069566934874,1.0,True,
10,12,2.8783779741216167,57.12161955993242,0.6945982527377876,0.10596349826421764,6,4.493186221276381,0.005660464094292755,0.08952914345108036,1.0,True,
10,13,3.84803303865234,78.07081297783375,0.6530587074494326,0.1098871098886565,6,4.997105968288151,0.009441275754405344,0.06902225135435515,1.0,True,
11,12,0.636223282239137,20.20192940175253,0.865792610250298,0.09248705530398661,6,3.4380496057859116,0.007654923324833218,0.03083951917063289,1.0,True,
11,13,2.0145868546459504,41.151122819653864,0.8217656194003106,0.10443913508460159,6,3.957969004992005,0.0019958122994113434,0.04243931133970266,1.0,True,
11,14,1.2405786051647103,116.67752660647822,0.7462918660287081,0.10754571546972236,6,4.619857754383166,0.0030226348073827927,0.05528144266002643,1.0,True,
12,13,1.5928240921286525,20.949193417901323,0.8347322378531742,0.10791137431619166,6,3.358528082155117,0.0033753816186126483,0.015427430722607398,1.0,True,
12,14,0.6774809171975742,96.47559720472566,0.7626657196969697,0.10926443071446633,6,3.847953795189004,0.0011083901763643633,0.03408686226954418,1.0,True,
12,15,1.8892292086221258,131.14857460490867,0.7404945183111733,0.10470183850479874,6,3.1806996321008576,0.008442575945215317,0.02844707164454242,1.0,True,
13,14,0.9721300146850552,75.52640378682433,0.8138569225293629,0.1125369920515294,6,3.2147734820704015,0.005457899756974233,0.04250371025579654,1.0,True,
13,15,2.426469315960194,110.19938118700728,0.7853886616014026,0.10710393317615087,6,2.4365756135744383,0.0023363953219779758,0.003679886698275418,1.0,True,
13,16,4.157137927296689,135.66277163100625,0.7461059190031153,0.10791684095774041,6,2.2141337125272007,0.0023169831632091926,0.02473862097872449,1.0,True,
14,15,2.176233486689304,34.672977400182965,0.7456694756554307,0.11049010248131722,6,2.3376174680383377,0.0027632919724097076,0.05272762900968177,1.0,True,
14,16,3.79947855245853,60.13636784418198,0.7114577084583084,0.11170286188791147,6,2.479974772421199,0.0043726035924820505,0.11525585711885118,1.0,True,
14,17,6.190474035537447,15.261944565117341,0.6170986278878855,0.11803645680912848,6,5.206426513766648,0.02802927019183766,0.10048860643286558,1.0,True,
15,16,1.7328875589135755,25.463390443999018,0.7687034629476421,0.09834033850996364,6,2.3729567416081196,0.004926598691892943,0.10049788043616295,1.0,True,
15,17,8.352264787019024,19.411032835065622,0.5596902808274586,0.12817180913542037,6,8.442537153126064,0.04660873899639204,0.08128930035257874,1.0,True,
15,18,6.346037230040679,25.592965647994788,0.6134687462217386,0.11861991782546771,6,5.933642175309751,0.006137337753783083,0.040498570689394164,1.0,True,
16,17,9.849975560032544,44.87442327906463,0.589649455234486,0.12715782204597983,6,10.240964391202342,0.016491489842564398,0.29283865924681673,1.0,True,
16,18,7.9099649541476005,0.12957520399577063,0.6441329694864798,0.11977657776801591,6,7.973620675877663,0.01581702082704062,0.07681086634765077,1.0,True,
16,19,6.609737623005951,34.172763646089344,0.642055375405338,0.11922626532224312,6,5.690723701436521,0.01936736899083179,0.22155509936605944,1.0,True,
17,18,2.081596655954183,45.003998483060414,0.761206687666586,0.10248796635270672,6,4.778521884225018,0.003602819302929225,0.04280787772842571,1.0,True,
17,19,4.34928579963815,79.047186925154,0.6654080389768575,0.10560001932239894,6,4.647516519431804,0.015683211248127868,0.18051709633786264,1.0,True,
17,20,4.4206846692505035,169.37257900760153,0.6377810007251632,0.11215519048187066,6,5.178731668470772,0.016926609942259613,0.20634229894075337,1.0,True,
18,19,3.459222336215112,34.04318844209358,0.7556208482370976,0.10994458141969204,6,3.594858191146124,0.004654350735545165,0.12873243616635754,1.0,True,
18,20,4.94806857498194,124.36858052454107,0.6686517992904207,0.11525187440612464,6,4.510107356424666,0.022993775653731053,0.1013964892640344,1.0,True,
18,21,6.5847443121227816,175.25342056617163,0.5905541093343251,0.1262853354637165,6,5.942345924349615,0.021857815102479028,0.4259066161744867,1.0,True,
19,20,3.031758915138065,90.32539208244746,0.7420998980632009,0.12052023632629244,6,3.3463968507926847,0.012566250876576632,0.08500209892702067,1.0,True,
19,21,3.732423384308353,141.21023212407198,0.6515019319456562,0.12055764747498063,6,4.076270140910382,0.007120603737667027,0.05561876226347328,1.0,True,
19,22,6.37255124464243,175.13200822775684,0.5905501782985226,0.12548545004843537,6,5.705777346141328,0.005772061596466365,0.10356450735473072,1.0,True,
20,21,2.057726146362386,50.884840041624386,0.7791517249907258,0.1109933103332085,6,3.525554312141476,0.004882992437392809,0.09954673132586826,1.0,True,
20,22,4.388457127616061,94.54259968980386,0.6575290550783224,0.11893104898070972,6,4.203674621017558,0.005511015274661758,0.13108161960065734,1.0,True,
20,23,8.256548639868363,126.4903843444735,0.5799237611181702,0.12732395079162134,6,6.120182481367245,0.02421237470645105,0.2728497258921342,1.0,True,
21,22,2.665930009787955,43.65775964817948,0.7379804721295267,0.11301282071283571,6,4.252070885311591,0.005342970038949662,0.023692959917636515,1.0,True,
21,23,6.670349114267683,75.60554430284907,0.6154133001864512,0.11796945645750438,6,5.503558762374886,0.021862964848253088,0.18671115674675795,1.0,True,
21,24,4.0761181240750135,118.87493116884215,0.6688601936925751,0.11271893154362743,6,4.6072424136923145,0.017882487871653564,0.11231417140662198,1.0,True,
22,23,4.005228418650323,31.947784654669576,0.7269494538989077,0.11696827793053062,6,3.9354343956207307,0.036708381465019306,0.17149160359500737,1.0,True,
22,24,1.4231800329066895,75.21717152066267,0.7625269567423569,0.11122908154278312,6,2.9923801902407035,0.033708002403797385,0.18898419347187523,1.0,True,
22,25,0.9824487562406952,165.63918634111403,0.7063042657606644,0.1109686247290255,6,3.260862218157852,0.0237546710240931,0.20388046327310663,1.0,True,
23,24,2.6253440212817054,43.26938686599312,0.785377057547531,0.11259637754562075,6,3.1689777161216344,0.008388924730327863,0.09824473511740778,1.0,True,
23,25,4.30404511575867,133.69140168644446,0.682445260093659,0.11585305930039552,6,4.459805342354154,0.03262070035564156,0.26820774135203534,1.0,True,
23,26,6.580252552934289,159.52196102005493,0.6190717727618564,0.1225437625254746,6,4.748664745468758,0.020450511219618477,0.11876511292532045,1.0,True,
24,25,2.0155105379126432,90.4220148204513,0.7662452591656131,0.10751986617832841,6,3.2073569479179214,0.020801978066425954,0.11499836231449187,1.0,True,
24,26,4.344602404326578,157.20865211395216,0.7051298290056998,0.11713046861462412,6,4.0423396253765445,0.02866879831073192,0.14020373652670878,1.0,True,
24,27,4.194437499675352,171.65485957819877,0.7231216797369087,0.11332956637109663,6,3.5737137574022637,0.0391123663878616,0.31745415833557195,1.0,True,
25,26,2.340922525153354,66.78663729350096,0.8146752104535746,0.1067005622933313,6,2.8555177924942416,0.015155094735859666,0.09937091691588736,1.0,True,
25,27,2.187150933555911,97.92312560134954,0.8480552070263488,0.10282262567757873,6,2.8008487935140383,0.020178532297188766,0.14259764688967286,1.0,True,
25,28,6.688406442287195,171.26720999836604,0.6516274978006786,0.11571138405667894,6,3.5101673009694134,0.012932261164889271,0.1340627397278959,1.0,True,
26,27,0.6349061963265976,31.136488307848563,0.8667504714016342,0.10966038141955464,6,2.9297274048756186,0.0016799531047772863,0.021489450960699312,1.0,True,
26,28,5.008419104260455,121.94615270813352,0.7044824981113069,0.11528623479803272,6,2.991781333569699,0.011526585390891731,0.12425176309998268,1.0,True,
26,29,2.224643201350574,145.73622267800252,0.713654161930024,0.11136018082941974,6,3.2605126809731075,0.0026952303885011083,0.026655596724504224,1.0,True,
27,28,5.622863551235285,90.80966440028486,0.7058971457311706,0.1123767383226778,6,3.2376046779603893,0.004853942846193662,0.04151426276015074,1.0,True,
27,29,2.458639091699311,114.59973437015381,0.7564328960645812,0.11469589052522192,6,3.205131133212321,0.012664149059349924,0.06847473063899887,1.0,True,
27,30,2.614522132166758,143.98503586800658,0.7694542698332492,0.10812761282109745,6,3.5636250205999853,0.029100131201629794,0.2714584305620497,1.0,True,
28,29,5.5254251571777075,23.790069969868927,0.6751737207833228,0.11779777341629802,6,4.576892329120707,0.02178556321929983,0.16690946122682282,1.0,True,
28,30,6.682895390289165,53.17537146772172,0.6491205871188156,0.12153627244194264,6,5.217063769204219,0.03128065038149053,0.11076032475862632,1.0,True,
28,31,11.27338119162288,23.882243944837096,0.5547279383916173,0.13098667045941628,6,8.538477868201968,0.007811024872033181,0.25693805699014277,1.0,True,
29,30,1.1998189814142899,29.3853014978528,0.8093424727088093,0.11457686827315132,6,2.757583581977887,0.011632920497220726,0.07792616982135933,1.0,True,
29,31,5.755712819539279,0.09217397496817598,0.7190982776089159,0.1179079898277715,6,4.190353867263325,0.010102013331328315,0.05295397679545269,1.0,True,
29,32,7.3497160995478215,44.7412737155501,0.683451384417257,0.12293657670032715,6,4.583568719289398,0.015683401555203327,0.1727804303986473,1.0,True,
30,31,4.592128513798009,29.29312752288462,0.7552004058853374,0.1130974670000902,6,3.6871413543680527,0.010357085925134528,0.12088315402655661,1.0,True,
30,32,6.2188831730504885,74.12657521340287,0.7014059073906874,0.11707558829895812,6,4.009905097582674,0.044382968752358344,0.22757601248033596,1.0,True,
30,33,7.344850966128434,112.7430048900433,0.6023929471032745,0.12334051659083738,6,4.260065691019417,0.002643716461244746,0.05342399356841621,1.0,True,
31,32,1.7043840283934533,44.83344769051828,0.785355810063055,0.11197405173054666,6,3.256332960817364,0.017947904056538493,0.11867936940390052,1.0,True,
31,33,3.3472002644811774,83.44987736715868,0.6260366926363408,0.11401170513833872,6,3.204107721135536,0.005088227871807909,0.05996016929359153,1.0,True,
31,34,3.311630046755356,133.49479301359594,0.6536974685122833,0.11166700866683012,6,3.289691648920709,0.01957685602042411,0.06586346108113965,1.0,True,
32,33,1.9394369188755196,38.61642967664041,0.7442187300370512,0.11487243540634488,6,2.5670787340169654,0.00371510987119075,0.04386474659625542,1.0,True,
32,34,2.1435446082722334,88.6613453230776,0.7408722109533469,0.1145103812359268,6,2.7396880211128143,0.0033854042879271646,0.04345206885157784,1.0,True,
32,35,3.1027737257249126,119.84526039487966,0.8249047681597268,0.10651397196801952,6,2.6908216879663684,0.014229552741777998,0.06789311177543367,1.0,True,
33,34,0.5704890065278277,50.0449156464372,0.8265015479876161,0.1079360614731508,6,3.0056080815747785,0.005517776656180037,0.04447304678975396,1.0,True,
33,35,2.3843314966611664,81.22883071823928,0.7029487179487179,0.11079043369762126,6,2.804837502264682,0.0045321280438173584,0.02066446888667937,1.0,True,
33,36,3.5605240220038255,106.66041349586102,0.612932138284251,0.11677728789370431,6,3.2326527799764184,0.011704878383874686,0.04254924292122366,1.0,True,
34,35,2.9403442697824866,31.183915071802076,0.7198829665436968,0.11381984857774002,6,3.296185466097946,0.015242618456135898,0.1219677981121258,1.0,True,
34,36,4.102710388907318,56.61549784942384,0.6450260449752255,0.11677131628998773,6,3.6854553885932066,0.0029445631740664846,0.0910859237401823,1.0,True,
34,37,3.6865711881345757,30.12086856551724,0.6870731404445548,0.11846537329500156,6,4.5399964456315365,0.013941395463696922,0.12307688004234886,1.0,True,
35,36,1.2045561534352058,25.431582777621774,0.811216429699842,0.11156358795429076,6,2.5821119008039335,0.003966448406861189,0.11486278330291146,1.0,True,
35,37,6.084062615492111,1.063046506284832,0.6916945230136282,0.12226449151266647,6,3.9062079497745845,0.012490325532322776,0.1588294259454204,1.0,True,
36,37,7.2781244258509386,26.494629283906598,0.6406009244992296,0.123972843683949,6,4.59789964279208,0.011995188628592031,0.19550165133048591,1.0,True,
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 0.503306788400079 13.198324485965863 0.7947725072604066 0.09714510386272111 6 3.1645559924308744 0.00040681040655899406 0.015836974306118006 1.0 True
3 0 2 1.1601020961002007 26.306295080591493 0.8196783588704571 0.10111548255452249 6 3.1496351272437915 0.0024786662018318395 0.04305117802148083 1.0 True
4 0 3 1.1328421148283978 41.74134818989305 0.8170202208162759 0.10132889132333661 6 2.9636847958606816 0.001763337668740155 0.0152261810177797 1.0 True
5 1 2 0.6927979865343085 13.107970594625625 0.7847728726807421 0.08513837010948266 6 2.6465075635845983 0.003621881376895638 0.011153493139183525 1.0 True
6 1 3 0.7002295454253883 28.543023703927183 0.7885699962401304 0.09713251502799032 6 2.6341764735488145 0.0014094469663425105 0.02902143941632062 1.0 True
7 1 4 0.8045641659338967 57.731023816949346 0.7363658899355479 0.09863727794945644 6 2.7377680197189993 0.011592408190980286 0.17084480097436408 1.0 True
8 2 3 0.13949776780243606 15.435053109301553 0.918286915396742 0.07288259780013887 6 2.6383381348446657 0.002273707715582919 0.0026923349652342365 1.0 True
9 2 4 0.6701726006967371 44.62305322232371 0.8370341514089079 0.09441127521663696 6 3.0939727970407334 0.0041401597740440875 0.03075174550917053 1.0 True
10 2 5 0.8004226998626774 62.96656300910673 0.8235371293623721 0.10626795357026747 6 2.8027866429960917 0.009148796674106823 0.05256412309062332 1.0 True
11 3 4 0.5366157827594368 29.188000113022152 0.8850603941513032 0.07613142655207121 6 2.6417590251999084 0.0013904645484531576 0.0259404694028417 1.0 True
12 3 5 0.6760093701980526 47.53150989980517 0.8740046236835346 0.09072202522560696 6 2.686157540984516 0.006509933107990933 0.02545212192512678 1.0 True
13 3 6 1.4361364806393455 80.09091141471853 0.6795928884308168 0.10271649349323747 6 3.008166890660092 0.00828154435242913 0.026486510290724834 1.0 True
14 4 5 0.17861948949299924 18.343509786783002 0.8684444444444445 0.0784387700526441 6 2.6861954442048104 0.0025072579888539386 0.021042298559759365 1.0 True
15 4 6 1.5807073315069207 50.90291130169636 0.6781301745000636 0.10313081772493418 6 2.8455217243212387 0.003271195715147848 0.14716032493381534 1.0 True
16 4 7 1.7804201387535592 75.18775779751417 0.7873548387096774 0.09891997778736471 6 3.1802938248442474 0.004633577062239209 0.04472721629193465 1.0 True
17 5 6 1.5316987521992935 32.55940151491337 0.6911991765311374 0.10152400788085783 6 3.2393513551418907 0.005288907067525738 0.05494235971756111 1.0 True
18 5 7 1.6091514152462776 56.844248010731185 0.7747360187719984 0.09121609616459708 6 3.123473300580222 0.00421464356720393 0.015634168997181876 1.0 True
19 5 8 1.1872485760158462 85.67585439000777 0.7829477514946712 0.10577559680144696 6 3.109219016133088 0.0033105282814613057 0.030813019102438145 1.0 True
20 6 7 1.5692385974903325 24.284846495817817 0.7347979599843075 0.10196810239533251 6 3.458846242963224 0.004020707752206965 0.039850323489805044 1.0 True
21 6 8 0.5035712140385419 53.11645287509441 0.7155149934810952 0.10460245111398395 6 3.0180670962260874 0.0072368624874882504 0.0055907688631446776 1.0 True
22 6 9 0.8508108569116883 66.44324365209327 0.6825438366919655 0.10495957402394829 6 3.140616320789838 0.00430894236478819 0.13911475434755866 1.0 True
23 7 8 1.1230406357307108 28.831606379276582 0.8127890077949531 0.09942756972076687 6 2.9436564519387325 0.008316114154115147 0.01814737917470093 1.0 True
24 7 9 0.7186795671679719 42.158397156275456 0.8193873491579367 0.09507291602212191 6 3.5012345051117295 0.006751593996528964 0.029293578235221863 1.0 True
25 7 10 0.39293571356254015 58.25702665650087 0.7940216681895313 0.10356080767641777 6 2.817840996766873 0.006883567504360684 0.016492683422577963 1.0 True
26 8 9 0.4407680716864947 13.326790776998866 0.8581438392384981 0.09484304022144129 6 3.219825075793889 0.007253035671159373 0.024252719250072975 1.0 True
27 8 10 0.968166048306331 29.425420277224287 0.8015356585111921 0.10152298131640612 6 3.7446585062478155 0.0017523919908150464 0.05042176209078739 1.0 True
28 8 11 3.0540124804140354 66.34511043540417 0.688687555052221 0.10536230082368817 6 4.696625415382456 0.0036281146181207165 0.013078005975049522 1.0 True
29 9 10 0.5276649044820116 16.098629500225417 0.8560606060606061 0.09594368034796862 6 3.2778548903058375 0.010491037599418197 0.0599937188173442 1.0 True
30 9 11 2.680593454060616 53.0183196584053 0.6966788735951509 0.10384409271666459 6 4.3419055531595605 0.009543265092514783 0.09977883713501384 1.0 True
31 9 12 3.2944321799753977 73.22024906015783 0.6579243162233046 0.10621203405076814 6 4.293885241325807 0.0028782078295197843 0.0743495992598884 1.0 True
32 10 11 2.249454986440387 36.91969015817989 0.729195173529046 0.10869447817964137 6 4.322382098282597 0.0028206125960216254 0.014140069566934874 1.0 True
33 10 12 2.8783779741216167 57.12161955993242 0.6945982527377876 0.10596349826421764 6 4.493186221276381 0.005660464094292755 0.08952914345108036 1.0 True
34 10 13 3.84803303865234 78.07081297783375 0.6530587074494326 0.1098871098886565 6 4.997105968288151 0.009441275754405344 0.06902225135435515 1.0 True
35 11 12 0.636223282239137 20.20192940175253 0.865792610250298 0.09248705530398661 6 3.4380496057859116 0.007654923324833218 0.03083951917063289 1.0 True
36 11 13 2.0145868546459504 41.151122819653864 0.8217656194003106 0.10443913508460159 6 3.957969004992005 0.0019958122994113434 0.04243931133970266 1.0 True
37 11 14 1.2405786051647103 116.67752660647822 0.7462918660287081 0.10754571546972236 6 4.619857754383166 0.0030226348073827927 0.05528144266002643 1.0 True
38 12 13 1.5928240921286525 20.949193417901323 0.8347322378531742 0.10791137431619166 6 3.358528082155117 0.0033753816186126483 0.015427430722607398 1.0 True
39 12 14 0.6774809171975742 96.47559720472566 0.7626657196969697 0.10926443071446633 6 3.847953795189004 0.0011083901763643633 0.03408686226954418 1.0 True
40 12 15 1.8892292086221258 131.14857460490867 0.7404945183111733 0.10470183850479874 6 3.1806996321008576 0.008442575945215317 0.02844707164454242 1.0 True
41 13 14 0.9721300146850552 75.52640378682433 0.8138569225293629 0.1125369920515294 6 3.2147734820704015 0.005457899756974233 0.04250371025579654 1.0 True
42 13 15 2.426469315960194 110.19938118700728 0.7853886616014026 0.10710393317615087 6 2.4365756135744383 0.0023363953219779758 0.003679886698275418 1.0 True
43 13 16 4.157137927296689 135.66277163100625 0.7461059190031153 0.10791684095774041 6 2.2141337125272007 0.0023169831632091926 0.02473862097872449 1.0 True
44 14 15 2.176233486689304 34.672977400182965 0.7456694756554307 0.11049010248131722 6 2.3376174680383377 0.0027632919724097076 0.05272762900968177 1.0 True
45 14 16 3.79947855245853 60.13636784418198 0.7114577084583084 0.11170286188791147 6 2.479974772421199 0.0043726035924820505 0.11525585711885118 1.0 True
46 14 17 6.190474035537447 15.261944565117341 0.6170986278878855 0.11803645680912848 6 5.206426513766648 0.02802927019183766 0.10048860643286558 1.0 True
47 15 16 1.7328875589135755 25.463390443999018 0.7687034629476421 0.09834033850996364 6 2.3729567416081196 0.004926598691892943 0.10049788043616295 1.0 True
48 15 17 8.352264787019024 19.411032835065622 0.5596902808274586 0.12817180913542037 6 8.442537153126064 0.04660873899639204 0.08128930035257874 1.0 True
49 15 18 6.346037230040679 25.592965647994788 0.6134687462217386 0.11861991782546771 6 5.933642175309751 0.006137337753783083 0.040498570689394164 1.0 True
50 16 17 9.849975560032544 44.87442327906463 0.589649455234486 0.12715782204597983 6 10.240964391202342 0.016491489842564398 0.29283865924681673 1.0 True
51 16 18 7.9099649541476005 0.12957520399577063 0.6441329694864798 0.11977657776801591 6 7.973620675877663 0.01581702082704062 0.07681086634765077 1.0 True
52 16 19 6.609737623005951 34.172763646089344 0.642055375405338 0.11922626532224312 6 5.690723701436521 0.01936736899083179 0.22155509936605944 1.0 True
53 17 18 2.081596655954183 45.003998483060414 0.761206687666586 0.10248796635270672 6 4.778521884225018 0.003602819302929225 0.04280787772842571 1.0 True
54 17 19 4.34928579963815 79.047186925154 0.6654080389768575 0.10560001932239894 6 4.647516519431804 0.015683211248127868 0.18051709633786264 1.0 True
55 17 20 4.4206846692505035 169.37257900760153 0.6377810007251632 0.11215519048187066 6 5.178731668470772 0.016926609942259613 0.20634229894075337 1.0 True
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57 18 20 4.94806857498194 124.36858052454107 0.6686517992904207 0.11525187440612464 6 4.510107356424666 0.022993775653731053 0.1013964892640344 1.0 True
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results/01_previous_two_batches/open3d_gicp/B_batch2_quality.json
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results/01_previous_two_batches/open3d_gicp/B_batch2_refined.npz
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results/01_previous_two_batches/open3d_gicp/B_batch2_refined.refinement.json
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results/01_previous_two_batches/open3d_gicp/batch1_auxiliary_check.json
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results/01_previous_two_batches/open3d_gicp/extrinsic_batch2.json
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results/01_previous_two_batches/open3d_gicp/extrinsic_batch2_refined.json
-534
View File
@@ -1,534 +0,0 @@
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results/01_previous_two_batches/small_gicp/B_batch1_auxiliary.npz
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i,j,rtk_translation_m,rtk_rotation_deg,heldout_inlier_ratio,heldout_inlier_rmse_m,hessian_rank,hessian_condition,reverse_translation_m,reverse_rotation_deg,multistart_success_rate,accepted,rejection_reasons
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0,3,5.800196618465403,50.885974946353855,0.7186066691459031,0.11701588647718943,6,18.363958192984455,0.008227849157773135,0.05354665376691754,1.0,True,
1,2,4.892046307546544,10.426600007662538,0.7423043095866315,0.11327867264180574,6,26.05793878332379,0.01880830643908174,0.13698190350335646,1.0,True,
1,3,1.7315947839093804,26.273333329665036,0.8389134554643083,0.10681097771213023,6,14.956194090958816,0.0035146019341874887,0.1349785173147079,1.0,True,
1,4,1.6946255625760025,60.40345832959281,0.88,0.10289486740770283,6,11.829281279209892,0.006064649004018339,0.061683233541617866,1.0,True,
2,3,3.2805755806518446,36.69993333732758,0.795020120724346,0.10968948046836192,6,23.96630628312886,0.006456546110122383,0.030970536985363332,1.0,True,
2,4,4.866335020594746,70.83005833725535,0.7190493965409979,0.11680954298455397,6,23.730154915082874,0.005632827193528356,0.03284052493715785,1.0,True,
2,5,1.8274406190287722,30.448549979291712,0.7598566308243727,0.11774293624986373,6,16.599379025063065,0.028141707120452414,0.19962462817569399,1.0,True,
3,4,1.75881263274858,34.13012499992777,0.83898411109721,0.10512726552504312,6,15.664666117555983,0.006186049607122224,0.016880074405568043,1.0,True,
3,5,2.1020200425132134,67.14848331661929,0.7968186901951038,0.11623997504357782,6,18.1548400244513,0.021983524103315014,0.3960603757299183,1.0,True,
3,6,5.791805311102367,61.163322289150074,0.6568381430363864,0.12435814704811046,6,25.0151910725651,0.008738020425158864,0.1291569973379011,1.0,True,
4,5,3.30592832159088,101.27860831654709,0.7543039842597147,0.11308907219490882,6,19.690455732375476,0.002243469564109645,0.06843841908142442,1.0,True,
4,6,6.453995240105232,95.29344728907782,0.6218497827436374,0.12814112793605542,6,41.84651093530334,0.01600824572236542,0.4490447249637784,1.0,True,
4,7,9.537609568572432,26.32130681988008,0.6169457128361238,0.13205154815512993,6,36.92786505190044,0.019160428104594212,0.2888349968548246,1.0,True,
5,6,3.7769167422851373,5.985161027469212,0.7372055740535208,0.1175976253407229,6,19.32269960027907,0.013833903611932107,0.01952771029395653,1.0,True,
5,7,6.565348405821576,74.95730149666699,0.6841773746535651,0.11964187966504931,6,27.33180748081407,0.003240486196092054,0.04738439683023512,1.0,True,
5,8,6.3343271669452825,107.99940292217995,0.6521066412758867,0.12472719695199193,6,19.156993592870748,0.0278691016568626,0.41056933218195657,1.0,True,
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6,8,3.3979014114419868,102.01424189471071,0.683003003003003,0.11564979549315224,6,20.447019845630425,0.008147035817338538,0.038229943243018406,1.0,True,
6,9,0.28270347875173524,124.11602215258995,0.7360264227642277,0.11829700338913093,6,18.21588471622476,0.003942462178993821,0.004948404195791413,1.0,True,
7,8,4.78580085090425,33.04210142551296,0.6644591611479028,0.12091344193997677,6,22.65051252921267,0.010087854632047892,0.1144705976875931,1.0,True,
7,9,2.876695741106431,166.91183737821345,0.6157641864692897,0.12124276796057824,6,31.889839980753695,2.858085971145254,5.202834749913653,1.0,False,forward_reverse_translation;forward_reverse_rotation
7,10,3.5227760858025783,151.5297683494061,0.8421725239616613,0.10512064341715703,6,18.208137447441548,0.00590396821278501,0.011716048666689089,1.0,True,
8,9,3.469611483011297,133.86973595269956,0.5997550520514391,0.12251997518546373,6,26.58434328826198,0.1455308769088698,0.6888512968600221,1.0,False,forward_reverse_translation;forward_reverse_rotation
8,10,8.226583602288494,175.42813022508852,0.5915068162625166,0.1229044134151169,6,28.111420003057543,0.008997663249148741,0.15439288797351725,1.0,True,
8,11,1.865267488778983,173.7180803410843,0.6836137963957513,0.11276660610764273,6,37.61742547586071,0.1113060196402131,0.53316971567076,1.0,False,forward_reverse_translation;forward_reverse_rotation
9,10,5.488028285806731,41.558394272381754,0.5969882593159775,0.11948988234049264,6,38.89573848099908,0.0030407162313759195,0.04107484998318919,1.0,True,
9,11,4.718056434111957,39.84834438838189,0.593742114559677,0.12239484647700946,6,46.81089854115947,0.035258275490363276,0.11274694382482037,1.0,True,
9,12,4.0664516687652705,11.300244475855449,0.6153363453815262,0.12504774785186165,6,23.277303065116303,0.0752860327247449,0.25939502405614967,1.0,True,
10,11,8.48963239412477,1.7100498839998592,0.5888198757763975,0.12435475144629402,6,51.68982317778118,0.008996147539897087,0.2187503499467401,1.0,True,
10,12,7.8510898708654135,30.258149796526293,0.5523974295600593,0.12600876663407296,6,24.040230371790127,0.0067913142930030295,0.19210976651930364,1.0,True,
10,13,7.303425556850936,93.89697482620899,0.48622589531680444,0.12387372192061825,6,40.15734453396082,0.010457065237222635,0.051939983599844905,1.0,True,
11,12,0.684356309096518,28.54809991252644,0.8081153752138841,0.09827968392237502,6,14.074429682972724,0.003170825002365958,0.052994059649638055,1.0,True,
11,13,4.983180422136547,92.18692494220912,0.4904655770183259,0.13065086109778945,6,59.94917005604024,0.03026165336674197,0.022229096602302635,1.0,True,
11,14,7.513058208926106,116.17598776756182,0.5502994743918836,0.13363854913882675,6,76.59552817138297,0.012728663920096705,0.13777836936248603,1.0,True,
12,13,4.470221622128888,63.638825029682685,0.5394185760039418,0.1291861812905223,6,40.18929295270992,0.016569656651710556,0.06971243680146068,1.0,True,
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12,15,9.425648786798952,64.11974003309916,0.512344920771404,0.13771827771927672,6,83.08102250810028,0.1594220156320846,0.7727589642419826,1.0,False,forward_reverse_translation;forward_reverse_rotation
13,14,2.6059162757855927,23.989062825352686,0.6685967722064802,0.11944947118678222,6,17.494241627788213,0.00889966253307221,0.1868503363813343,1.0,True,
13,15,4.985522367267706,0.4809150034164723,0.6380833851897947,0.12132550246856395,6,19.285114979899365,0.011310526444604854,0.11618951108877962,1.0,True,
13,16,5.330126890587504,0.570225046299944,0.5094008523439458,0.12969283093489314,6,23.287780580490335,0.01929138406979036,0.6381307294537529,1.0,False,forward_reverse_rotation
14,15,2.3845573265941185,23.508147821936216,0.7915233415233415,0.11297936021484274,6,14.802453343324077,0.04714013527415902,0.5990777245962995,1.0,False,forward_reverse_rotation
14,16,7.871327660405588,24.55928787165262,0.515650129902264,0.1258655742770286,6,27.98905523695805,0.019790578845351906,0.2492103704468241,1.0,True,
14,17,6.334147330965719,0.13339551798522414,0.6016802569804793,0.12195151942370595,6,25.851285634799194,0.01939980613812204,0.1992615163030762,1.0,True,
15,16,10.188241681839806,1.051140049716416,0.4590676165479315,0.13331101566670972,6,37.36189113980903,0.03193705319987942,0.25820325147713563,1.0,True,
15,17,8.639041695194567,23.641543339921444,0.5389415876185721,0.1297159188720241,6,23.825996467364362,0.007152603768137945,0.01099978555369691,1.0,True,
15,18,9.620687246763701,76.25281000098852,0.5202642612120442,0.13378678930694327,6,39.71428865706424,0.023698091864950893,0.7026335973530661,1.0,False,forward_reverse_rotation
16,17,1.5531467972267479,24.69268338963786,0.7229415461973602,0.10903876418375444,6,27.565368871601265,0.004743603748938733,0.03110066538977425,1.0,True,
16,18,0.5677742310084086,77.30395005070493,0.75774251343742,0.10672872147965808,6,23.961833593584473,0.00729627345064471,0.5370128665073076,1.0,False,forward_reverse_rotation
16,19,2.9811390265794215,109.91770005034847,0.624375,0.11606003815798548,6,43.92870457834769,0.00942822069733503,0.10349184268425643,1.0,True,
17,18,0.98603936348044,52.61126666106708,0.8035782747603833,0.11258525838778834,6,10.5263751279628,0.04456291633933312,0.40754887525204303,1.0,True,
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17,20,1.702964560097507,146.95247656520317,0.6696552595024624,0.11181113630432839,6,22.83917826847431,0.009851652453759501,0.030403861001117326,1.0,True,
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18,20,1.0853574230860805,94.34120990413592,0.7890203137053227,0.110891959946164,6,9.73093989119612,0.07641393239305394,0.480643469622101,1.0,True,
18,21,0.7190584934612743,134.43032499953063,0.7686106562539362,0.1151576675189467,6,11.132461972272983,0.012325022302827005,0.06184414805686676,1.0,True,
19,20,1.9962827272376449,61.72745990449234,0.7206177800100452,0.11075109929370276,6,18.593207910714877,0.06755956873701982,0.6275145294718035,1.0,False,forward_reverse_rotation
19,21,3.133843614009462,101.81657499988698,0.6599384615384616,0.1100348641166372,6,24.889550971173787,0.032753113036640115,0.1570816751009414,1.0,True,
19,22,3.558853688535236,131.87013333415302,0.6354319180087847,0.11655958277402616,6,35.20679617831266,0.011678083327496752,0.14115606360994692,1.0,True,
20,21,1.4312693216245356,40.08911509539463,0.8091622059006598,0.10860060068735904,6,9.844983425406634,0.004342811149796408,0.43079835067471983,1.0,True,
20,22,2.8745434122269677,70.1426734296607,0.7075518262586377,0.11619246575360609,6,10.41692741032582,0.03233653011234762,0.3501326921447133,1.0,True,
20,23,2.368927629183393,88.7954400941961,0.7127672722778395,0.11187552234677736,6,10.890181651202985,0.0036477332397017036,0.03588537530780425,1.0,True,
21,22,1.9888588003695142,30.053558334266054,0.7451905626134301,0.11103160300292489,6,9.138865693353942,0.007025231148999399,0.031656114821116355,1.0,True,
21,23,1.9477165743251885,48.70632499880147,0.7537566650508968,0.1091762180475198,6,10.033043192754391,0.0026262958914527778,0.05939834892732321,1.0,True,
21,24,3.76075382790461,86.89547500633407,0.7108181370133071,0.1147630411233105,6,12.077810466162274,0.002611195084459223,0.042823121286843194,1.0,True,
22,23,0.8778876913955667,18.652766664535406,0.8809323561215908,0.09065520084082093,6,9.426617095196006,0.00074030291113835,0.005760006892562891,1.0,True,
22,24,2.2510479983846268,56.84191667206801,0.7459386832783681,0.1116686389188481,6,10.471980698049531,0.010212372381123916,0.01461127103474581,1.0,True,
22,25,3.452614527841686,105.36038662410485,0.6250595521676989,0.10935727133461083,6,47.514851436189325,0.013489723519689117,0.0723917800582207,1.0,True,
23,24,1.8212773036309853,38.18915000753262,0.7936138977244923,0.11107653584131949,6,10.660643490600723,0.008437497431588122,0.2765568737620497,1.0,True,
23,25,2.999856552735995,86.70761995956946,0.6337826553739712,0.11196536842375276,6,43.84810300726168,0.02359951283541323,0.10308564406068685,1.0,True,
23,26,1.7088514993141615,157.34900000166795,0.6073053892215569,0.11004156310392302,6,52.70992994760862,0.013062761031411258,0.0548185753269731,1.0,True,
24,25,1.2059352225587747,48.51846995203683,0.7898629804777495,0.11080878069613129,6,17.323052863790767,0.024355899665769485,0.10833606018329535,1.0,True,
24,26,1.1958827550030364,119.15984999413537,0.7515217920623326,0.10216449744519186,6,27.478293204817643,0.010967953426658109,0.044641472084512177,1.0,True,
24,27,3.786115539231191,149.59794999698258,0.6259589210591437,0.11074683701330018,6,26.105827828207687,0.04374270593226806,0.34168251600562055,1.0,True,
25,26,1.8270495459501794,70.64138004209858,0.8273774189718628,0.10613402567174812,6,17.478877580241615,0.018863132797671257,0.12251687299132978,1.0,True,
25,27,4.741885618180033,101.07948004494568,0.6236883367506936,0.11275084173352168,6,26.65509822252546,0.005757137825848521,0.2558617834036415,1.0,True,
25,28,3.8640843382258447,141.59343004213218,0.5871679316888045,0.11732003715351383,6,52.15762909847497,1.0486443236448235,4.536374847153588,1.0,False,forward_reverse_translation;forward_reverse_rotation
26,27,2.939444415707082,30.43810000284707,0.6585129571324776,0.10848902314415616,6,30.227639491425478,0.002959097196576797,0.043581628031778105,1.0,True,
26,28,2.285770454773634,70.95205000003368,0.6648410525062507,0.11120026543063653,6,45.565898082132684,0.012512803173543077,0.11122589391716618,1.0,True,
26,29,3.6568094249627374,131.92299999999997,0.5895513507080804,0.11693751976202812,6,62.1400238672027,2.7672344834891307,9.168367379205225,0.0,False,forward_reverse_translation;forward_reverse_rotation;multistart_instability
27,28,2.3764785758716087,40.513949997186636,0.7954517962985364,0.10154002419986238,6,26.01517111533084,0.019135551099374308,0.08227568287606397,1.0,True,
27,29,4.061689152213973,101.48489999715295,0.7446499818643453,0.1104935519181424,6,32.698094109014136,0.015390416284740782,0.08645102756020058,1.0,True,
27,30,5.033742765789926,178.61309998505533,0.583363515634432,0.11446907648011068,6,105.65813856703667,0.017491035693048888,0.22327775856236398,0.0,False,multistart_instability
28,29,1.7860052935263149,60.970949999966315,0.8317879220161674,0.10401467258245657,6,16.856539687156317,0.007126704551442278,0.059447117802805614,1.0,True,
28,30,7.378510482049102,138.09914998778677,0.540360873694207,0.1217315596532435,6,243.03186464572013,0.02476143939627759,0.2783665026489561,1.0,True,
28,31,8.396001985859645,161.15751666826222,0.5051186318198242,0.12667907013046822,6,145.12677311467613,0.01381623315543165,0.08866229443960012,1.0,True,
29,30,8.925911734961222,77.12819998782052,0.5223065970574277,0.12780035193537048,6,94.88866430179526,0.06232017355575957,1.955625956099923,1.0,False,forward_reverse_rotation
29,31,9.936139535050964,100.18656666829587,0.49440231130371975,0.12866710903574613,6,130.4988571288183,0.047123741889749056,0.2295904679982487,1.0,True,
29,32,9.719184595429152,159.1482027457047,0.49802134548507015,0.12675987702934546,6,49.64602672737782,0.0323736334045635,0.06974950067660468,0.0,False,multistart_instability
30,31,1.0176267260390144,23.058366680475352,0.8480586608967424,0.09667505139943416,6,22.5712438700004,0.01573204986385798,0.04061482397026774,1.0,True,
30,32,0.8427696971904044,82.02000275788396,0.867680517303317,0.0986734040048058,6,23.367311395821964,0.002023273028745447,0.014811950864385802,1.0,True,
30,33,1.1320092061720382,147.1799498371321,0.7995722941665676,0.10006346131537393,6,41.688755802373954,0.00887908049317058,0.048438711468841336,1.0,True,
31,32,0.2771314063233405,58.9616360774086,0.8486026731470231,0.09714101002609693,6,27.412782801978597,0.012921368358244189,0.05809273577687571,1.0,True,
31,33,0.5409306841643147,124.12158315665668,0.07871263259402121,0.14967084978243636,6,33.0912130689722,2.900706563387122,12.11541439568944,1.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
31,34,0.9901318542574941,155.5976733552555,0.7813404050144648,0.09211161518215291,6,17.31754498769328,3.351107529092291,8.098567652508597,0.0,False,forward_reverse_translation;forward_reverse_rotation;multistart_instability
32,33,0.38765596924480794,65.15994707924807,0.8592699327569645,0.08972486966187442,6,22.68434781952533,0.0035725430520846596,0.009168450597030125,1.0,True,
32,34,1.0327853226589305,96.63603727784675,0.8328530259365994,0.09815070141607997,6,23.927327238925002,0.04150636593087017,1.4498937658029205,1.0,False,forward_reverse_rotation
32,35,1.1947451269258382,131.6892972449946,0.8031309297912713,0.09391797152520207,6,30.896133075774756,0.007719527793580684,0.006042163579184511,0.0,False,multistart_instability
33,34,0.7453275493729801,31.47609019859864,0.8676435549201811,0.0964383255266311,6,26.835745049910184,0.02602048161602559,1.951142742234491,1.0,False,forward_reverse_rotation
33,35,1.0924186931915323,66.52935016574651,0.8329018592610026,0.09179860947142506,6,17.934600324801995,0.0072384331805243046,0.028209706778456432,1.0,True,
33,36,3.335020341384374,123.21987515531416,0.760797342192691,0.09861359027653524,6,26.32312350297475,0.0022735557932631,0.12221117701824208,1.0,True,
34,35,0.6186130964444055,35.05325996714787,0.8649093904448105,0.0788491357224537,6,23.02575456989985,0.0032110784907267375,0.015379666997505564,1.0,True,
34,36,3.114273556969299,91.74378495671547,0.7729789590254706,0.09845381444357724,6,36.91664640012635,0.011794412591703963,0.006135949966801849,1.0,True,
34,37,2.8155528331227235,126.08390993626574,0.722881252293017,0.09759501600127507,6,42.82845764024001,0.024742240312777743,0.08718512569927171,1.0,True,
35,36,2.4989423629184655,56.69052498956759,0.7695298262665533,0.09495386680108024,6,27.978829086780696,0.006644101856716534,0.038610475450088305,1.0,True,
35,37,2.2182956249874577,91.03064996911786,0.7217727327617437,0.10621413586570766,6,32.52725359303394,0.04503438705750094,1.5781812743250268,1.0,False,forward_reverse_rotation
36,37,0.5129211474320938,34.34012497955026,0.8785082174462705,0.08834636280188386,6,23.324824248042262,0.003787237363253161,0.027331592166342466,1.0,True,
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 4.069460063439237 24.612641616688823 0.7376764924448849 0.11846276711125131 6 14.186383674198092 0.007238562624113292 0.02790414903884654 1.0 True
3 0 2 8.883155687691525 14.186041609026283 0.617186536801874 0.12801123590557925 6 36.79061260583155 0.006865219918284302 0.20055597955733165 1.0 True
4 0 3 5.800196618465403 50.885974946353855 0.7186066691459031 0.11701588647718943 6 18.363958192984455 0.008227849157773135 0.05354665376691754 1.0 True
5 1 2 4.892046307546544 10.426600007662538 0.7423043095866315 0.11327867264180574 6 26.05793878332379 0.01880830643908174 0.13698190350335646 1.0 True
6 1 3 1.7315947839093804 26.273333329665036 0.8389134554643083 0.10681097771213023 6 14.956194090958816 0.0035146019341874887 0.1349785173147079 1.0 True
7 1 4 1.6946255625760025 60.40345832959281 0.88 0.10289486740770283 6 11.829281279209892 0.006064649004018339 0.061683233541617866 1.0 True
8 2 3 3.2805755806518446 36.69993333732758 0.795020120724346 0.10968948046836192 6 23.96630628312886 0.006456546110122383 0.030970536985363332 1.0 True
9 2 4 4.866335020594746 70.83005833725535 0.7190493965409979 0.11680954298455397 6 23.730154915082874 0.005632827193528356 0.03284052493715785 1.0 True
10 2 5 1.8274406190287722 30.448549979291712 0.7598566308243727 0.11774293624986373 6 16.599379025063065 0.028141707120452414 0.19962462817569399 1.0 True
11 3 4 1.75881263274858 34.13012499992777 0.83898411109721 0.10512726552504312 6 15.664666117555983 0.006186049607122224 0.016880074405568043 1.0 True
12 3 5 2.1020200425132134 67.14848331661929 0.7968186901951038 0.11623997504357782 6 18.1548400244513 0.021983524103315014 0.3960603757299183 1.0 True
13 3 6 5.791805311102367 61.163322289150074 0.6568381430363864 0.12435814704811046 6 25.0151910725651 0.008738020425158864 0.1291569973379011 1.0 True
14 4 5 3.30592832159088 101.27860831654709 0.7543039842597147 0.11308907219490882 6 19.690455732375476 0.002243469564109645 0.06843841908142442 1.0 True
15 4 6 6.453995240105232 95.29344728907782 0.6218497827436374 0.12814112793605542 6 41.84651093530334 0.01600824572236542 0.4490447249637784 1.0 True
16 4 7 9.537609568572432 26.32130681988008 0.6169457128361238 0.13205154815512993 6 36.92786505190044 0.019160428104594212 0.2888349968548246 1.0 True
17 5 6 3.7769167422851373 5.985161027469212 0.7372055740535208 0.1175976253407229 6 19.32269960027907 0.013833903611932107 0.01952771029395653 1.0 True
18 5 7 6.565348405821576 74.95730149666699 0.6841773746535651 0.11964187966504931 6 27.33180748081407 0.003240486196092054 0.04738439683023512 1.0 True
19 5 8 6.3343271669452825 107.99940292217995 0.6521066412758867 0.12472719695199193 6 19.156993592870748 0.0278691016568626 0.41056933218195657 1.0 True
20 6 7 3.1558607172566147 68.97214046919777 0.7538481109273629 0.11470554120698542 6 17.160170663404163 0.03433121044907878 0.14981218442224864 1.0 True
21 6 8 3.3979014114419868 102.01424189471071 0.683003003003003 0.11564979549315224 6 20.447019845630425 0.008147035817338538 0.038229943243018406 1.0 True
22 6 9 0.28270347875173524 124.11602215258995 0.7360264227642277 0.11829700338913093 6 18.21588471622476 0.003942462178993821 0.004948404195791413 1.0 True
23 7 8 4.78580085090425 33.04210142551296 0.6644591611479028 0.12091344193997677 6 22.65051252921267 0.010087854632047892 0.1144705976875931 1.0 True
24 7 9 2.876695741106431 166.91183737821345 0.6157641864692897 0.12124276796057824 6 31.889839980753695 2.858085971145254 5.202834749913653 1.0 False forward_reverse_translation;forward_reverse_rotation
25 7 10 3.5227760858025783 151.5297683494061 0.8421725239616613 0.10512064341715703 6 18.208137447441548 0.00590396821278501 0.011716048666689089 1.0 True
26 8 9 3.469611483011297 133.86973595269956 0.5997550520514391 0.12251997518546373 6 26.58434328826198 0.1455308769088698 0.6888512968600221 1.0 False forward_reverse_translation;forward_reverse_rotation
27 8 10 8.226583602288494 175.42813022508852 0.5915068162625166 0.1229044134151169 6 28.111420003057543 0.008997663249148741 0.15439288797351725 1.0 True
28 8 11 1.865267488778983 173.7180803410843 0.6836137963957513 0.11276660610764273 6 37.61742547586071 0.1113060196402131 0.53316971567076 1.0 False forward_reverse_translation;forward_reverse_rotation
29 9 10 5.488028285806731 41.558394272381754 0.5969882593159775 0.11948988234049264 6 38.89573848099908 0.0030407162313759195 0.04107484998318919 1.0 True
30 9 11 4.718056434111957 39.84834438838189 0.593742114559677 0.12239484647700946 6 46.81089854115947 0.035258275490363276 0.11274694382482037 1.0 True
31 9 12 4.0664516687652705 11.300244475855449 0.6153363453815262 0.12504774785186165 6 23.277303065116303 0.0752860327247449 0.25939502405614967 1.0 True
32 10 11 8.48963239412477 1.7100498839998592 0.5888198757763975 0.12435475144629402 6 51.68982317778118 0.008996147539897087 0.2187503499467401 1.0 True
33 10 12 7.8510898708654135 30.258149796526293 0.5523974295600593 0.12600876663407296 6 24.040230371790127 0.0067913142930030295 0.19210976651930364 1.0 True
34 10 13 7.303425556850936 93.89697482620899 0.48622589531680444 0.12387372192061825 6 40.15734453396082 0.010457065237222635 0.051939983599844905 1.0 True
35 11 12 0.684356309096518 28.54809991252644 0.8081153752138841 0.09827968392237502 6 14.074429682972724 0.003170825002365958 0.052994059649638055 1.0 True
36 11 13 4.983180422136547 92.18692494220912 0.4904655770183259 0.13065086109778945 6 59.94917005604024 0.03026165336674197 0.022229096602302635 1.0 True
37 11 14 7.513058208926106 116.17598776756182 0.5502994743918836 0.13363854913882675 6 76.59552817138297 0.012728663920096705 0.13777836936248603 1.0 True
38 12 13 4.470221622128888 63.638825029682685 0.5394185760039418 0.1291861812905223 6 40.18929295270992 0.016569656651710556 0.06971243680146068 1.0 True
39 12 14 7.042432348069883 87.62788785503537 0.5838501763346711 0.13149007038687927 6 46.68047224389145 0.016475747103725697 0.027479170527625274 1.0 True
40 12 15 9.425648786798952 64.11974003309916 0.512344920771404 0.13771827771927672 6 83.08102250810028 0.1594220156320846 0.7727589642419826 1.0 False forward_reverse_translation;forward_reverse_rotation
41 13 14 2.6059162757855927 23.989062825352686 0.6685967722064802 0.11944947118678222 6 17.494241627788213 0.00889966253307221 0.1868503363813343 1.0 True
42 13 15 4.985522367267706 0.4809150034164723 0.6380833851897947 0.12132550246856395 6 19.285114979899365 0.011310526444604854 0.11618951108877962 1.0 True
43 13 16 5.330126890587504 0.570225046299944 0.5094008523439458 0.12969283093489314 6 23.287780580490335 0.01929138406979036 0.6381307294537529 1.0 False forward_reverse_rotation
44 14 15 2.3845573265941185 23.508147821936216 0.7915233415233415 0.11297936021484274 6 14.802453343324077 0.04714013527415902 0.5990777245962995 1.0 False forward_reverse_rotation
45 14 16 7.871327660405588 24.55928787165262 0.515650129902264 0.1258655742770286 6 27.98905523695805 0.019790578845351906 0.2492103704468241 1.0 True
46 14 17 6.334147330965719 0.13339551798522414 0.6016802569804793 0.12195151942370595 6 25.851285634799194 0.01939980613812204 0.1992615163030762 1.0 True
47 15 16 10.188241681839806 1.051140049716416 0.4590676165479315 0.13331101566670972 6 37.36189113980903 0.03193705319987942 0.25820325147713563 1.0 True
48 15 17 8.639041695194567 23.641543339921444 0.5389415876185721 0.1297159188720241 6 23.825996467364362 0.007152603768137945 0.01099978555369691 1.0 True
49 15 18 9.620687246763701 76.25281000098852 0.5202642612120442 0.13378678930694327 6 39.71428865706424 0.023698091864950893 0.7026335973530661 1.0 False forward_reverse_rotation
50 16 17 1.5531467972267479 24.69268338963786 0.7229415461973602 0.10903876418375444 6 27.565368871601265 0.004743603748938733 0.03110066538977425 1.0 True
51 16 18 0.5677742310084086 77.30395005070493 0.75774251343742 0.10672872147965808 6 23.961833593584473 0.00729627345064471 0.5370128665073076 1.0 False forward_reverse_rotation
52 16 19 2.9811390265794215 109.91770005034847 0.624375 0.11606003815798548 6 43.92870457834769 0.00942822069733503 0.10349184268425643 1.0 True
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56 18 19 3.0423123591285863 32.61374999964355 0.7803049555273189 0.10890373850857962 6 14.866928278077225 0.0027074586252039488 0.03345524421318835 1.0 True
57 18 20 1.0853574230860805 94.34120990413592 0.7890203137053227 0.110891959946164 6 9.73093989119612 0.07641393239305394 0.480643469622101 1.0 True
58 18 21 0.7190584934612743 134.43032499953063 0.7686106562539362 0.1151576675189467 6 11.132461972272983 0.012325022302827005 0.06184414805686676 1.0 True
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60 19 21 3.133843614009462 101.81657499988698 0.6599384615384616 0.1100348641166372 6 24.889550971173787 0.032753113036640115 0.1570816751009414 1.0 True
61 19 22 3.558853688535236 131.87013333415302 0.6354319180087847 0.11655958277402616 6 35.20679617831266 0.011678083327496752 0.14115606360994692 1.0 True
62 20 21 1.4312693216245356 40.08911509539463 0.8091622059006598 0.10860060068735904 6 9.844983425406634 0.004342811149796408 0.43079835067471983 1.0 True
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67 21 24 3.76075382790461 86.89547500633407 0.7108181370133071 0.1147630411233105 6 12.077810466162274 0.002611195084459223 0.042823121286843194 1.0 True
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71 23 24 1.8212773036309853 38.18915000753262 0.7936138977244923 0.11107653584131949 6 10.660643490600723 0.008437497431588122 0.2765568737620497 1.0 True
72 23 25 2.999856552735995 86.70761995956946 0.6337826553739712 0.11196536842375276 6 43.84810300726168 0.02359951283541323 0.10308564406068685 1.0 True
73 23 26 1.7088514993141615 157.34900000166795 0.6073053892215569 0.11004156310392302 6 52.70992994760862 0.013062761031411258 0.0548185753269731 1.0 True
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76 24 27 3.786115539231191 149.59794999698258 0.6259589210591437 0.11074683701330018 6 26.105827828207687 0.04374270593226806 0.34168251600562055 1.0 True
77 25 26 1.8270495459501794 70.64138004209858 0.8273774189718628 0.10613402567174812 6 17.478877580241615 0.018863132797671257 0.12251687299132978 1.0 True
78 25 27 4.741885618180033 101.07948004494568 0.6236883367506936 0.11275084173352168 6 26.65509822252546 0.005757137825848521 0.2558617834036415 1.0 True
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80 26 27 2.939444415707082 30.43810000284707 0.6585129571324776 0.10848902314415616 6 30.227639491425478 0.002959097196576797 0.043581628031778105 1.0 True
81 26 28 2.285770454773634 70.95205000003368 0.6648410525062507 0.11120026543063653 6 45.565898082132684 0.012512803173543077 0.11122589391716618 1.0 True
82 26 29 3.6568094249627374 131.92299999999997 0.5895513507080804 0.11693751976202812 6 62.1400238672027 2.7672344834891307 9.168367379205225 0.0 False forward_reverse_translation;forward_reverse_rotation;multistart_instability
83 27 28 2.3764785758716087 40.513949997186636 0.7954517962985364 0.10154002419986238 6 26.01517111533084 0.019135551099374308 0.08227568287606397 1.0 True
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85 27 30 5.033742765789926 178.61309998505533 0.583363515634432 0.11446907648011068 6 105.65813856703667 0.017491035693048888 0.22327775856236398 0.0 False multistart_instability
86 28 29 1.7860052935263149 60.970949999966315 0.8317879220161674 0.10401467258245657 6 16.856539687156317 0.007126704551442278 0.059447117802805614 1.0 True
87 28 30 7.378510482049102 138.09914998778677 0.540360873694207 0.1217315596532435 6 243.03186464572013 0.02476143939627759 0.2783665026489561 1.0 True
88 28 31 8.396001985859645 161.15751666826222 0.5051186318198242 0.12667907013046822 6 145.12677311467613 0.01381623315543165 0.08866229443960012 1.0 True
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90 29 31 9.936139535050964 100.18656666829587 0.49440231130371975 0.12866710903574613 6 130.4988571288183 0.047123741889749056 0.2295904679982487 1.0 True
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92 30 31 1.0176267260390144 23.058366680475352 0.8480586608967424 0.09667505139943416 6 22.5712438700004 0.01573204986385798 0.04061482397026774 1.0 True
93 30 32 0.8427696971904044 82.02000275788396 0.867680517303317 0.0986734040048058 6 23.367311395821964 0.002023273028745447 0.014811950864385802 1.0 True
94 30 33 1.1320092061720382 147.1799498371321 0.7995722941665676 0.10006346131537393 6 41.688755802373954 0.00887908049317058 0.048438711468841336 1.0 True
95 31 32 0.2771314063233405 58.9616360774086 0.8486026731470231 0.09714101002609693 6 27.412782801978597 0.012921368358244189 0.05809273577687571 1.0 True
96 31 33 0.5409306841643147 124.12158315665668 0.07871263259402121 0.14967084978243636 6 33.0912130689722 2.900706563387122 12.11541439568944 1.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
97 31 34 0.9901318542574941 155.5976733552555 0.7813404050144648 0.09211161518215291 6 17.31754498769328 3.351107529092291 8.098567652508597 0.0 False forward_reverse_translation;forward_reverse_rotation;multistart_instability
98 32 33 0.38765596924480794 65.15994707924807 0.8592699327569645 0.08972486966187442 6 22.68434781952533 0.0035725430520846596 0.009168450597030125 1.0 True
99 32 34 1.0327853226589305 96.63603727784675 0.8328530259365994 0.09815070141607997 6 23.927327238925002 0.04150636593087017 1.4498937658029205 1.0 False forward_reverse_rotation
100 32 35 1.1947451269258382 131.6892972449946 0.8031309297912713 0.09391797152520207 6 30.896133075774756 0.007719527793580684 0.006042163579184511 0.0 False multistart_instability
101 33 34 0.7453275493729801 31.47609019859864 0.8676435549201811 0.0964383255266311 6 26.835745049910184 0.02602048161602559 1.951142742234491 1.0 False forward_reverse_rotation
102 33 35 1.0924186931915323 66.52935016574651 0.8329018592610026 0.09179860947142506 6 17.934600324801995 0.0072384331805243046 0.028209706778456432 1.0 True
103 33 36 3.335020341384374 123.21987515531416 0.760797342192691 0.09861359027653524 6 26.32312350297475 0.0022735557932631 0.12221117701824208 1.0 True
104 34 35 0.6186130964444055 35.05325996714787 0.8649093904448105 0.0788491357224537 6 23.02575456989985 0.0032110784907267375 0.015379666997505564 1.0 True
105 34 36 3.114273556969299 91.74378495671547 0.7729789590254706 0.09845381444357724 6 36.91664640012635 0.011794412591703963 0.006135949966801849 1.0 True
106 34 37 2.8155528331227235 126.08390993626574 0.722881252293017 0.09759501600127507 6 42.82845764024001 0.024742240312777743 0.08718512569927171 1.0 True
107 35 36 2.4989423629184655 56.69052498956759 0.7695298262665533 0.09495386680108024 6 27.978829086780696 0.006644101856716534 0.038610475450088305 1.0 True
108 35 37 2.2182956249874577 91.03064996911786 0.7217727327617437 0.10621413586570766 6 32.52725359303394 0.04503438705750094 1.5781812743250268 1.0 False forward_reverse_rotation
109 36 37 0.5129211474320938 34.34012497955026 0.8785082174462705 0.08834636280188386 6 23.324824248042262 0.003787237363253161 0.027331592166342466 1.0 True
results/01_previous_two_batches/small_gicp/B_batch1_quality.json
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i,j,rtk_translation_m,rtk_rotation_deg,heldout_inlier_ratio,heldout_inlier_rmse_m,hessian_rank,hessian_condition,reverse_translation_m,reverse_rotation_deg,multistart_success_rate,accepted,rejection_reasons
0,1,0.503306788400079,13.198324485965863,0.8131655372700871,0.09008834340898543,6,11.429289586039127,0.0005504552049968047,0.0032916777115618487,1.0,True,
0,2,1.1601020961002007,26.306295080591493,0.8167658604533367,0.09980570916927256,6,11.00789089021616,0.00205395151868756,0.012799633971915293,1.0,True,
0,3,1.1328421148283978,41.74134818989305,0.81441508497705,0.10115686777955255,6,13.837529680361882,0.01247476852224996,0.1726729736128094,1.0,True,
0,4,1.0129799192122786,70.92934830291522,0.7761963190184049,0.10269905695661055,6,12.888313335228592,0.019300920937430674,0.12217052794626229,1.0,True,
0,5,0.9014321094792815,89.27285808969826,0.7735100978813034,0.10986502690369805,6,24.919203290408266,0.0278108340699398,0.2717481445912482,1.0,True,
1,2,0.6927979865343085,13.107970594625625,0.7850287907869482,0.08649388452503427,6,11.757459931907107,0.001960394451155616,0.002128143929965857,1.0,True,
1,3,0.7002295454253883,28.543023703927183,0.7901992730918661,0.09109387779670658,6,12.066030386554463,0.004906057223105903,0.018773640398341965,1.0,True,
1,4,0.8045641659338967,57.731023816949346,0.7439266236985622,0.09680131928922782,6,12.860596463339792,0.020081946886066578,1.2340284819813434,1.0,False,forward_reverse_rotation
1,5,0.7829726518389362,76.07453360373235,0.7313806483915384,0.10148815288043825,6,12.782489568272775,0.016729134900242502,0.06734086907642674,1.0,True,
1,6,0.7843480712672012,108.63393511864574,0.6214689265536724,0.10742176387842234,6,26.39069479810122,0.0027238824581310127,0.04004688680398286,1.0,True,
2,3,0.13949776780243606,15.435053109301553,0.9176300578034682,0.07082931569628453,6,11.740335220665598,0.0016419682635548487,0.0015051284247814798,1.0,True,
2,4,0.6701726006967371,44.62305322232371,0.840540189585768,0.08212714345330237,6,12.917292889611646,0.0015059170263523442,0.004861145455894842,1.0,True,
2,5,0.8004226998626774,62.96656300910673,0.8300970873786407,0.0931389934573794,6,15.74858194202481,0.002555949510569182,0.008486377461290648,1.0,True,
2,6,1.3845111356101025,95.5259645240201,0.6495130297446696,0.10354826871538153,6,15.262952717632835,0.008359384079660144,0.07441619593698878,1.0,True,
2,7,2.161304865409727,119.81081101983789,0.8143257302921169,0.09968516416989152,6,30.540416925007207,0.013121403719342412,0.05039604031914888,1.0,True,
3,4,0.5366157827594368,29.188000113022152,0.8855689764780674,0.07250679559229746,6,11.672391892657863,0.0006574625393083392,0.0008548453445728261,1.0,True,
3,5,0.6760093701980526,47.53150989980517,0.8759311584895967,0.08543306637795112,6,15.073465050917047,0.0011073394938326227,0.004974962253885575,1.0,True,
3,6,1.4361364806393455,80.09091141471853,0.678819891780469,0.10239967326707564,6,23.156860004682848,0.002691305620040523,0.027681404773136364,1.0,True,
3,7,2.1008551524156123,104.37575791053635,0.7961101683853283,0.10180989325686585,6,29.756525470837225,0.023292005056525338,0.174639961337377,1.0,True,
3,8,1.3108481779402037,133.20736428981294,0.11556480999479438,0.1433106939673773,6,147.35707874920627,1.368389528072986,15.0277123732284,1.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
4,5,0.17861948949299924,18.343509786783002,0.8685714285714285,0.07604325543622346,6,12.370184068840063,0.003904582263467037,0.011742002375313174,1.0,True,
4,6,1.5807073315069207,50.90291130169636,0.6822060883963826,0.10567824422734254,6,18.163522769519293,0.011923922180697284,0.08008579261790838,1.0,True,
4,7,1.7804201387535592,75.18775779751417,0.7963870967741935,0.10178391918503964,6,25.315987799570973,0.034377900701078094,0.11532260919614924,1.0,True,
4,8,1.2818497736845669,104.01936417679076,0.7430850379518847,0.10189238984319506,6,25.19640114706588,0.034977663635135596,1.5753470469755222,1.0,False,forward_reverse_rotation
4,9,1.5141865499370524,117.34615495378958,0.0841833440929632,0.15176056899798168,6,84.88516221869183,3.340127368239112,6.177861200193235,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
5,6,1.5316987521992935,32.55940151491337,0.6864384971693258,0.10315026388508003,6,11.505426214947052,0.002552272421554712,0.013899678900349944,1.0,True,
5,7,1.6091514152462776,56.844248010731185,0.8104549602398644,0.09862238510016193,6,16.517299930207333,0.0037398303757535147,0.02129636862756259,1.0,True,
5,8,1.1872485760158462,85.67585439000777,0.7806082661814401,0.10305777405801758,6,17.737819341361416,0.0056799616182010805,0.026182015145595202,1.0,True,
5,9,1.380208837139288,99.00264516700663,0.7946777980693973,0.10313535699942489,6,14.147499646203187,0.06270036555847405,0.33857191756776234,1.0,True,
5,10,1.7506315276318887,115.10127466723206,0.7847418443359877,0.10018431236036145,6,21.425526326745782,0.005291671170548772,0.044796841001461946,1.0,True,
6,7,1.5692385974903325,24.284846495817817,0.7380672159016608,0.1046107504689679,6,13.947054238965462,0.010178220865611676,0.026255002379027764,1.0,True,
6,8,0.5035712140385419,53.11645287509441,0.7026075619295958,0.10337241424878332,6,21.232848227388097,0.017447450845101884,0.1292664848366822,1.0,True,
6,9,0.8508108569116883,66.44324365209327,0.6714210939544621,0.10949604778423808,6,34.21205543097956,0.09491078892598759,0.32255986251320673,1.0,False,forward_reverse_translation
6,10,1.3331914459292633,82.5418731523187,0.6619427982478743,0.10510077922086615,6,31.101566437428897,0.003562383894956786,0.04536548719264196,1.0,True,
6,11,3.16887584646763,119.46156331049859,0.638006230529595,0.1061575017783152,6,31.943306843588733,0.0059304520508679575,0.05963953472442965,1.0,True,
7,8,1.1230406357307108,28.831606379276582,0.8040692297529396,0.09680052952350976,6,22.21980394474879,0.008328073875015064,0.06350723756646647,1.0,True,
7,9,0.7186795671679719,42.158397156275456,0.8274764620076913,0.09296346258223728,6,22.096355228992984,0.0076981468910999155,0.020277033650542,1.0,True,
7,10,0.39293571356254015,58.25702665650087,0.7992429186790236,0.10479299924479576,6,30.246845596558646,0.04240408437264065,1.1111803590641476,1.0,False,forward_reverse_rotation
7,11,2.4544901278439815,95.17671681468076,0.7502523977788995,0.1028589368143411,6,19.931284665592216,0.005620519728387707,0.0303880139721275,1.0,True,
7,12,3.0905023273107046,115.3786462164333,0.7054418372441338,0.10706084863640537,6,10.761985004268995,0.004629206980441498,0.07009059694702204,1.0,True,
8,9,0.4407680716864947,13.326790776998866,0.8539132734003173,0.09158655960119419,6,31.292808872701826,0.0036696927581956673,0.014909075167693285,1.0,True,
8,10,0.968166048306331,29.425420277224287,0.7989328474752733,0.09757506446655329,6,25.45934970841849,0.008689580645481346,0.052297435035790506,1.0,True,
8,11,3.0540124804140354,66.34511043540417,0.6854158802063672,0.10755838259974337,6,42.46956117899474,0.00234148729464824,0.061662020462905004,1.0,True,
8,12,3.652970846697932,86.5470398371567,0.6485376477909147,0.11031208403019,6,30.049664340525695,0.013830104055554564,0.11563630256710061,1.0,True,
8,13,4.357531202056024,107.49623325505803,0.622879241516966,0.1149201207874094,6,27.04257772502661,0.005835943764379069,0.23887217550290574,1.0,True,
9,10,0.5276649044820116,16.098629500225417,0.8486154649947754,0.09691837979566219,6,22.577834737399076,0.007052114296262031,0.2907164943632002,1.0,True,
9,11,2.680593454060616,53.0183196584053,0.6963000378835712,0.10535088240045436,6,39.530571540955094,0.0034527207661208545,0.020238883727394828,1.0,True,
9,12,3.2944321799753977,73.22024906015783,0.6584238791057825,0.10587631557960675,6,33.498316372288826,0.009482405807021465,0.07225186033919398,1.0,True,
9,13,4.1147924441994315,94.16944247805917,0.621557336004006,0.11335203160746431,6,32.6975845279655,0.009011781776631502,0.0416830306778097,1.0,True,
9,14,3.748520283907519,169.69584626488452,0.048890560361037984,0.15557197380465354,6,51.54107837707314,1.0864076801076206,7.184564913859941,0.5,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
10,11,2.249454986440387,36.91969015817989,0.7156362731683045,0.10422606491110974,6,45.45254669271086,0.004301999273657816,0.02361974788713562,1.0,True,
10,12,2.8783779741216167,57.12161955993242,0.692875599852344,0.10691573133186853,6,39.11010182412513,0.004231594662123499,0.01872424915403812,1.0,True,
10,13,3.84803303865234,78.07081297783375,0.6594721262950173,0.10871022234744057,6,43.1464607017966,0.006980776690426113,0.21408921607631748,1.0,True,
10,14,3.3834992499807997,153.59721676465847,0.062245276028158575,0.15570919185507412,6,59.4104405367878,0.09444016841800375,2.8556589503073977,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
10,15,1.4957886147833104,171.72980583515965,0.6232518545542989,0.10969245891269987,6,43.00146365447724,3.0001452916434883,4.8522273147326915,1.0,False,forward_reverse_translation;forward_reverse_rotation
11,12,0.636223282239137,20.20192940175253,0.8688915375446961,0.08660981428891965,6,18.033297623211222,0.005089530929434581,0.03170796903435614,1.0,True,
11,13,2.0145868546459504,41.151122819653864,0.8021741727392188,0.10650327571539893,6,18.339724002801702,0.04485533499047456,0.27313934813876123,1.0,True,
11,14,1.2405786051647103,116.67752660647822,0.7455741626794259,0.11063741423489376,6,20.694961222671232,0.007044503253911164,0.40985193451245056,1.0,True,
11,15,1.4202907399288436,151.3505040066613,0.7466415272213057,0.10329430650455694,6,27.704214205277534,0.13203399207663835,1.4478079414864902,0.5,False,forward_reverse_translation;forward_reverse_rotation
11,16,2.7368971716016928,176.81389445065594,0.78639603721155,0.10318237166131629,6,34.658693494341755,0.008767229441137649,0.032548654712657686,1.0,True,
12,13,1.5928240921286525,20.949193417901323,0.8217283366828231,0.10684146627686454,6,16.400323597466677,0.01763325318199382,0.09467538550034012,1.0,True,
12,14,0.6774809171975742,96.47559720472566,0.7611268939393939,0.10826784336909262,6,10.635638995146312,0.0015931090432955832,0.024585439481643892,1.0,True,
12,15,1.8892292086221258,131.14857460490867,0.7324469325868906,0.11113399174419211,6,19.970471882560926,0.0024416100752998565,0.009279978214820864,1.0,True,
12,16,3.349436875941103,156.6119650489076,0.7500298864315601,0.10525785108693746,6,30.70828038440084,0.0024719134275639418,0.013160987022490347,1.0,True,
12,17,6.51378754358264,111.73754176984298,0.6198434030618207,0.1221546776649581,6,23.54160456185754,0.013538123192296372,0.06236055877935909,1.0,True,
13,14,0.9721300146850552,75.52640378682433,0.8278562107011508,0.10919246415865995,6,12.723402368483741,0.0049872211162087675,0.02566897887278044,1.0,True,
13,15,2.426469315960194,110.19938118700728,0.7886616014026885,0.10800229196760061,6,13.66454080002457,0.0038816141140735755,0.020030260506224723,1.0,True,
13,16,4.157137927296689,135.66277163100625,0.7477833692786964,0.10901310110319071,6,21.034367079780026,0.0024107551433002806,0.01470679715840447,1.0,True,
13,17,6.347445048137873,90.78834835194164,0.6122042632935114,0.12126385638135445,6,27.550293797111443,0.010111269848200304,1.6132660739833655,1.0,False,forward_reverse_rotation
13,18,4.26917989710959,135.79234683500215,0.6756988719960765,0.1140499722068077,6,13.668694297489541,0.010188526275029454,0.05757819838859875,1.0,True,
14,15,2.176233486689304,34.672977400182965,0.7415730337078652,0.11426627617931512,6,13.437209845730706,0.009280638111078932,0.07287904157175247,1.0,True,
14,16,3.79947855245853,60.13636784418198,0.7130173965206958,0.11194096137848975,6,36.60672565292038,0.0004016408999195087,0.020284570842973915,1.0,True,
14,17,6.190474035537447,15.261944565117341,0.6118212736015011,0.11991020962112661,6,17.152314443231333,0.020151321868834806,0.2079666531947748,1.0,True,
14,18,4.170672674565236,60.26594304817776,0.6756292203806016,0.1138218459710723,6,11.041435170572107,0.023698120255922432,0.1321697884270417,1.0,True,
14,19,3.9999134794649294,94.30913149027137,0.6699172941612147,0.112413086617321,6,9.748384934395327,0.018186549996561333,1.2493862686891326,1.0,False,forward_reverse_rotation
15,16,1.7328875589135755,25.463390443999018,0.7714218177520388,0.10015132538295435,6,21.49868629780701,0.007439481433750378,0.036349268136368934,1.0,True,
15,17,8.352264787019024,19.411032835065622,0.5587657459840518,0.12832496127520593,6,38.53228682833441,0.005430301274456062,0.06530638270069017,1.0,True,
15,18,6.346037230040679,25.592965647994788,0.615040502962157,0.11840729454457544,6,21.032065383701333,0.012871328625232324,0.037753566213482644,1.0,True,
15,19,5.607512178689573,59.636154090088375,0.6234350309955026,0.11735435665832353,6,16.270394372537467,0.007833445352022574,0.016491686030251827,1.0,True,
15,20,8.633248754322691,149.96154617253603,0.5562658304185261,0.13192141621005607,6,26.944324835385952,0.023510650105734523,0.673469393155776,1.0,False,forward_reverse_rotation
16,17,9.849975560032544,44.87442327906463,0.5900047370914259,0.1265936504427481,6,90.52736834449948,0.011296955150750666,0.21501790849158783,1.0,True,
16,18,7.9099649541476005,0.12957520399577063,0.6471098982882659,0.12019779875781794,6,38.44752656622707,0.016821400599454826,0.06522344447557246,1.0,True,
16,19,6.609737623005951,34.172763646089344,0.6280868046894488,0.12242728727104996,6,33.04590657073916,0.06745241310870236,1.6183598806956783,1.0,False,forward_reverse_rotation
16,20,9.541288639020653,124.49815572853673,0.5791883197228409,0.127436475456937,6,40.945151692592816,2.599521231663455,5.87323656621683,1.0,False,forward_reverse_translation;forward_reverse_rotation
16,21,9.407850320517097,175.38299577015903,0.49424730531670097,0.13389808634231865,6,45.28912477989511,0.04478742358365585,0.10475809279093058,1.0,True,
17,18,2.081596655954183,45.003998483060414,0.7629028349890962,0.10358638519274684,6,18.62989302843653,0.007136146106563684,0.13887814859043496,1.0,True,
17,19,4.34928579963815,79.047186925154,0.6728380024360536,0.10727518915463004,6,33.81541258417489,0.0017207670327870018,0.035486867252565536,1.0,True,
17,20,4.4206846692505035,169.37257900760153,0.6354846507130771,0.11168219002675635,6,38.40506040461897,0.020290214498459216,0.2831192364677464,1.0,True,
17,21,6.400116212709096,139.7425809507742,0.5695348561959995,0.1231813524465837,6,57.78224861663297,0.009859208985419086,0.31053558178227947,1.0,True,
17,22,8.798226453379533,96.08482130259475,0.5459048079246195,0.1287223998113377,6,62.758312260043155,0.03194615937469955,0.5773515871730924,1.0,False,forward_reverse_rotation
18,19,3.459222336215112,34.04318844209358,0.7562595809913132,0.11139160446923307,6,9.605522638453952,0.013156152321531143,0.09121207325672914,1.0,True,
18,20,4.94806857498194,124.36858052454107,0.6668778509883426,0.1149564835181103,6,18.76442873201715,0.0076759006397633354,0.11713903044228172,1.0,True,
18,21,6.5847443121227816,175.25342056617163,0.5977438948803768,0.12625086545570646,6,26.666713369082085,0.021119392446408227,0.18202979634982344,1.0,True,
18,22,9.20681259274594,141.08881978565523,0.5286220871327254,0.13561310095446666,6,65.22594013516775,0.03713510940704989,0.6483189610135184,1.0,False,forward_reverse_rotation
18,23,13.164275822166362,109.14103513098563,0.47146496815286626,0.14168298172295804,6,112.91469628781756,0.01208598984738065,0.6082838532521024,1.0,False,forward_reverse_rotation
19,20,3.031758915138065,90.32539208244746,0.7409531090723751,0.11651827109675585,6,10.060869835729363,0.0051743650857167135,0.07263138471648796,1.0,True,
19,21,3.732423384308353,141.21023212407198,0.6563629565000623,0.12291187901196955,6,15.339627619255575,0.0075358808329252175,0.06620678153945667,1.0,True,
19,22,6.37255124464243,175.13200822775684,0.5891492613346918,0.12519472268444526,6,28.30213785955742,0.010212573357042046,0.11056129275235052,1.0,True,
19,23,10.37094757900695,143.1842235730793,0.4984627209838586,0.13443006540917718,6,96.22322799212152,0.02229275506707497,0.2903847316754494,1.0,True,
19,24,7.752290899896264,99.91483670708607,0.5732565579014716,0.1300803661721533,6,45.37964984018865,0.07078749161909308,0.6582469262123747,1.0,False,forward_reverse_rotation
20,21,2.057726146362386,50.884840041624386,0.7808828984790405,0.11119787612354914,6,10.584324886965474,0.0031476308503252674,0.022487168529706673,1.0,True,
20,22,4.388457127616061,94.54259968980386,0.6557604850934815,0.11898674179731801,6,16.883756700029306,0.008500396323601164,0.06463449106814506,1.0,True,
20,23,8.256548639868363,126.4903843444735,0.5833545108005083,0.12863144503040289,6,48.7242741462686,0.013890482176493085,0.14514813799569015,1.0,True,
20,24,5.797339988732605,169.75977121046614,0.6348521385962685,0.11831053318180502,6,31.671029215629257,0.00725649124030166,0.27346756317921195,1.0,True,
20,25,3.9584300095978864,99.81821396908215,0.7094296865164296,0.11461231093244224,6,16.207076145397426,0.005999722474434507,0.07369423294061743,1.0,True,
21,22,2.665930009787955,43.65775964817948,0.7387220368310469,0.11520069835112393,6,16.025252253059314,0.0021484382611444506,0.09312757978022868,1.0,True,
21,23,6.670349114267683,75.60554430284907,0.6238657551274084,0.11987801174807478,6,32.271874238810035,0.007927242611182222,0.08795608522524892,1.0,True,
21,24,4.0761181240750135,118.87493116884215,0.6712192699279861,0.11172772070549726,6,21.556821129992922,0.04673963175312233,0.22273623714194987,1.0,True,
21,25,2.626640779558244,150.70305401070652,0.699310174919931,0.11234398811200015,6,15.335715959290205,0.018678960705618183,0.1734016933410122,1.0,True,
21,26,2.086425835169367,83.91641671720558,0.7166481550043194,0.11091971646336789,6,12.109452825928004,0.00977162867106615,0.0631934229127065,1.0,True,
22,23,4.005228418650323,31.947784654669576,0.7252984505969012,0.11644876240570266,6,17.935878140214236,0.013002087545984397,0.11470841251550605,1.0,True,
22,24,1.4231800329066895,75.21717152066267,0.7555499175440822,0.1089594430160029,6,14.427403734239578,0.0044919719825295985,0.008669004448985662,1.0,True,
22,25,0.9824487562406952,165.63918634111403,0.7088209387190134,0.11165869814478939,6,23.409617344710625,0.005265657588642687,0.10195131255569051,1.0,True,
22,26,3.0743977770984916,127.57417636538506,0.6831820474029249,0.11509847268437542,6,18.05320621935197,0.010313466929779696,0.02189018950989812,1.0,True,
22,27,3.054692363582515,96.43768805753649,0.712325317889966,0.11297739003609018,6,14.570486778570737,0.006473104665270118,0.07627008321159866,1.0,True,
23,24,2.6253440212817054,43.26938686599312,0.787546254944494,0.11112536909525574,6,13.667766137918514,0.003984385308883342,0.012875657690538275,1.0,True,
23,25,4.30404511575867,133.69140168644446,0.6839640551828883,0.11561487899989795,6,40.22316701306096,0.009663940236368565,0.2587163051792124,1.0,True,
23,26,6.580252552934289,159.52196102005493,0.6214811057570377,0.12356096223549605,6,59.18789907295204,0.03346639663911815,0.040359288659068966,1.0,True,
23,27,6.261292051742304,128.3854727122061,0.6439154109155375,0.11883308044324468,6,48.252953818047224,0.01363202571969651,0.268813724284828,1.0,True,
23,28,10.83543880539923,37.57580831192121,0.5551750380517504,0.13104613123848732,6,44.00847715074386,0.01592501575703795,0.1925228188054137,1.0,True,
24,25,2.0155105379126432,90.4220148204513,0.7610619469026548,0.10446971824743242,6,22.29894236104597,0.01721789520038266,0.09266069416139829,1.0,True,
24,26,4.344602404326578,157.20865211395216,0.6986700443318556,0.11795525471114342,6,30.354013262701024,0.0239438468814135,0.2700005297192175,1.0,True,
24,27,4.194437499675352,171.65485957819877,0.7233746521629142,0.11505806966234582,6,24.218829066684407,0.0071603040929797065,0.1144565168286882,1.0,True,
24,28,8.235813904401468,80.84519517791432,0.618522741669834,0.1218895746871147,6,34.55835443937473,0.008956300116613691,0.24319664279778394,1.0,True,
24,29,2.7255497122534083,57.0551252080454,0.7635993899339095,0.11147922533242269,6,11.572525725181164,0.02246832420061709,0.4519372330281508,1.0,True,
25,26,2.340922525153354,66.78663729350096,0.8176906646563639,0.1076748622762494,6,10.194320056407282,0.011727583881002021,0.04299213457608308,1.0,True,
25,27,2.187150933555911,97.92312560134954,0.8508155583437892,0.10177690901936605,6,9.447407214356089,0.004921098494284399,0.04722324529000337,1.0,True,
25,28,6.688406442287195,171.26720999836604,0.6552720874701521,0.1158307449607044,6,16.856702471098828,0.010027166778796438,0.069841684352646,1.0,True,
25,29,1.411474020521721,147.47714002849685,0.7434442763489663,0.11227320270489505,6,19.207346965238134,0.005744598995027529,0.30413185830684,1.0,True,
25,30,0.5324917816481396,118.09183853064393,0.7986111111111112,0.1061010900656728,6,14.995080416656407,0.004651476537899485,0.06337779258524685,1.0,True,
26,27,0.6349061963265976,31.136488307848563,0.8835952231301069,0.10164781558403184,6,8.741658683232068,0.0010243697887392141,0.04630474119881922,1.0,True,
26,28,5.008419104260455,121.94615270813352,0.7013346764039284,0.1156303654422143,6,10.39669408260953,0.016692399239017493,0.1408617368634082,1.0,True,
26,29,2.224643201350574,145.73622267800252,0.7255273462170014,0.11343814488049828,6,14.171918272295303,0.0030631045126193056,0.10499072900350268,1.0,True,
26,30,2.6736935848107626,175.1215241758588,0.7273877292852625,0.11178149175643984,6,20.18904245623208,0.002906139936114469,0.06343916895540856,1.0,True,
26,31,6.867005915953627,145.82839665297058,0.6099962135554714,0.12348129729633385,6,43.35600938855713,0.011230201352754812,0.02547666380871279,1.0,True,
27,28,5.622863551235285,90.80966440028486,0.7116811266188859,0.11387091842867321,6,10.816842555490693,0.011430300098438593,0.17858658887118406,1.0,True,
27,29,2.458639091699311,114.59973437015381,0.7588294651866802,0.10898351788466304,6,11.452504275483305,0.002552411031283606,0.096053889700167,1.0,True,
27,30,2.614522132166758,143.98503586800658,0.7699595755432036,0.10809432324457112,6,19.384788093934308,0.006419845852952349,0.09683669503892821,1.0,True,
27,31,6.500901961576647,114.69190834512194,0.6407864885303756,0.12195597170279295,6,45.71099685307573,0.03203829198983217,0.21314212616538905,1.0,True,
27,32,8.204840364723921,69.85846065460369,0.6120722798923491,0.12793570356778294,6,50.652407095458955,0.02476270066797521,0.18953242722457034,1.0,True,
28,29,5.5254251571777075,23.790069969868927,0.6751737207833228,0.11816626627973714,6,15.58130572797597,0.0036062245918322845,0.04228106700822467,1.0,True,
28,30,6.682895390289165,53.17537146772172,0.6476021763887132,0.12162303323930705,6,19.56745364015458,0.036760870155111036,0.061189777619461184,1.0,True,
28,31,11.27338119162288,23.882243944837096,0.558515338972352,0.1324943729180927,6,46.74937334475135,0.011294835537302048,0.22869636591571527,1.0,True,
28,32,12.874594603353637,20.951203745681177,0.5356867779204108,0.13793317141107478,6,47.36276875018484,0.009260784922164039,0.4736145985672847,1.0,True,
28,33,13.796424177198949,59.567633422321585,0.49968659897204465,0.13776776698165452,6,59.65569689133837,0.03480709552965627,0.1328751318540718,1.0,True,
29,30,1.1998189814142899,29.3853014978528,0.8126428027418127,0.11465616151168853,6,9.73882704495904,0.059123063905753705,0.35885997079417975,1.0,True,
29,31,5.755712819539279,0.09217397496817598,0.7178318135764944,0.11831514282300001,6,19.572443598172608,0.03055388488044922,0.16788276176822323,1.0,True,
29,32,7.3497160995478215,44.7412737155501,0.6866709594333548,0.12344589693451308,6,19.69654687402097,0.01847461637742171,0.10764991453418553,1.0,True,
29,33,8.359102419083325,83.35770339219052,0.6151911468812877,0.12607024585552623,6,19.321252328369155,0.005640558537625966,0.060018922799470055,1.0,True,
29,34,8.008340805017072,133.40261903862776,0.6489454636216149,0.1229782406905996,6,19.361211916752247,0.024951598645873194,0.20519725330906424,1.0,True,
30,31,4.592128513798009,29.29312752288462,0.76103500761035,0.11418966560335815,6,16.756236396505788,0.010805499433410783,0.0764328342532002,1.0,True,
30,32,6.2188831730504885,74.12657521340287,0.7051463949438926,0.11749249261595905,6,20.554466965332146,0.009422682577629085,0.037900377279079585,1.0,True,
30,33,7.344850966128434,112.7430048900433,0.6064231738035264,0.12368925274094972,6,20.291143104650924,0.0147669225762263,0.09037435316445036,1.0,True,
30,34,7.0347870411064095,162.78792053648075,0.6427946506686664,0.12352381867411776,6,21.47516866844496,3.1821548176867474,2.4099911828321603,1.0,False,forward_reverse_translation;forward_reverse_rotation
30,35,9.281470813161304,166.02816439171798,0.690784364483562,0.12252550482763978,6,16.634587270964886,0.009824737708452339,0.14818916841264132,1.0,True,
31,32,1.7043840283934533,44.83344769051828,0.7959078625659504,0.10965414855273443,6,12.882067962817517,0.009608345699366565,0.04073745185785107,1.0,True,
31,33,3.3472002644811774,83.44987736715868,0.636089469716009,0.11810494837446742,6,13.701242758091343,0.01055590691460776,0.06271370554561609,1.0,True,
31,34,3.311630046755356,133.49479301359594,0.6599326599326599,0.11513164519765848,6,15.199690471396346,0.002580333972310516,0.03476275200242535,1.0,True,
31,35,4.805055361756335,164.67870808539848,0.7747933884297521,0.1099315236542533,6,13.983106845566315,0.002969158365974405,0.02482993261597952,1.0,True,
31,36,5.921091773191984,169.88970913698103,0.79326799071447,0.11165900641583826,6,12.181844458808802,0.004151989866605975,0.032942374858786365,1.0,True,
32,33,1.9394369188755196,38.61642967664041,0.7479238533282229,0.1140254726045761,6,12.862133545437658,0.005854383016193307,0.042356141718508426,1.0,True,
32,34,2.1435446082722334,88.6613453230776,0.7426470588235294,0.11481351455228676,6,15.077486603426832,0.0025843844761028077,0.009064526838353553,1.0,True,
32,35,3.1027737257249126,119.84526039487966,0.8209641402863523,0.10635864973089924,6,10.914008667831691,0.004447738370013908,0.11616784364821804,1.0,True,
32,36,4.235311686227635,145.27684317250132,0.7613800341073068,0.10898930843612145,6,12.22686011069426,0.008302372052489307,0.047221847858387886,1.0,True,
32,37,3.1931475050378357,118.78221388859481,0.6919923126201153,0.11497666348297753,6,16.91846428853567,0.00567166990415677,0.10190182649448634,1.0,True,
33,34,0.5704890065278277,50.0449156464372,0.8247678018575851,0.10774292174748101,6,13.307399451848953,0.005218203824516851,0.054034410494683056,1.0,True,
33,35,2.3843314966611664,81.22883071823928,0.7097435897435898,0.11216257584808116,6,12.920219167499932,0.0055472051152683725,0.06161504272529624,1.0,True,
33,36,3.5605240220038255,106.66041349586102,0.62202304737516,0.11282494351568334,6,18.491230751066993,0.009207361872581905,0.07430583689659953,1.0,True,
33,37,4.013080941901569,80.16578421195443,0.6328828828828829,0.12540757119118434,6,17.018187124475837,0.0036940550280262596,0.12927083795080543,1.0,True,
34,35,2.9403442697824866,31.183915071802076,0.7146673451214858,0.11428688542425412,6,13.705016766266674,0.013436446539858737,0.05510481554154184,1.0,True,
34,36,4.102710388907318,56.61549784942384,0.6465506288908652,0.11729490897001751,6,19.754334744118587,0.086056763787067,1.0836879727751445,1.0,False,forward_reverse_translation;forward_reverse_rotation
34,37,3.6865711881345757,30.12086856551724,0.6899292189246243,0.11937420197560855,6,16.808577408839643,0.005083360744530967,0.10024940937419655,1.0,True,
35,36,1.2045561534352058,25.431582777621774,0.8242496050552922,0.10907898456971946,6,9.660062538057792,0.0026096461304541156,0.07595057929078583,1.0,True,
35,37,6.084062615492111,1.063046506284832,0.6924659295448702,0.12103303062396852,6,14.36240888115935,0.005914867863779407,0.06573359298031699,1.0,True,
36,37,7.2781244258509386,26.494629283906598,0.6402157164869029,0.12413886977390769,6,20.08544982146282,0.008397869289519334,0.08479281990151419,1.0,True,
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 0.503306788400079 13.198324485965863 0.8131655372700871 0.09008834340898543 6 11.429289586039127 0.0005504552049968047 0.0032916777115618487 1.0 True
3 0 2 1.1601020961002007 26.306295080591493 0.8167658604533367 0.09980570916927256 6 11.00789089021616 0.00205395151868756 0.012799633971915293 1.0 True
4 0 3 1.1328421148283978 41.74134818989305 0.81441508497705 0.10115686777955255 6 13.837529680361882 0.01247476852224996 0.1726729736128094 1.0 True
5 0 4 1.0129799192122786 70.92934830291522 0.7761963190184049 0.10269905695661055 6 12.888313335228592 0.019300920937430674 0.12217052794626229 1.0 True
6 0 5 0.9014321094792815 89.27285808969826 0.7735100978813034 0.10986502690369805 6 24.919203290408266 0.0278108340699398 0.2717481445912482 1.0 True
7 1 2 0.6927979865343085 13.107970594625625 0.7850287907869482 0.08649388452503427 6 11.757459931907107 0.001960394451155616 0.002128143929965857 1.0 True
8 1 3 0.7002295454253883 28.543023703927183 0.7901992730918661 0.09109387779670658 6 12.066030386554463 0.004906057223105903 0.018773640398341965 1.0 True
9 1 4 0.8045641659338967 57.731023816949346 0.7439266236985622 0.09680131928922782 6 12.860596463339792 0.020081946886066578 1.2340284819813434 1.0 False forward_reverse_rotation
10 1 5 0.7829726518389362 76.07453360373235 0.7313806483915384 0.10148815288043825 6 12.782489568272775 0.016729134900242502 0.06734086907642674 1.0 True
11 1 6 0.7843480712672012 108.63393511864574 0.6214689265536724 0.10742176387842234 6 26.39069479810122 0.0027238824581310127 0.04004688680398286 1.0 True
12 2 3 0.13949776780243606 15.435053109301553 0.9176300578034682 0.07082931569628453 6 11.740335220665598 0.0016419682635548487 0.0015051284247814798 1.0 True
13 2 4 0.6701726006967371 44.62305322232371 0.840540189585768 0.08212714345330237 6 12.917292889611646 0.0015059170263523442 0.004861145455894842 1.0 True
14 2 5 0.8004226998626774 62.96656300910673 0.8300970873786407 0.0931389934573794 6 15.74858194202481 0.002555949510569182 0.008486377461290648 1.0 True
15 2 6 1.3845111356101025 95.5259645240201 0.6495130297446696 0.10354826871538153 6 15.262952717632835 0.008359384079660144 0.07441619593698878 1.0 True
16 2 7 2.161304865409727 119.81081101983789 0.8143257302921169 0.09968516416989152 6 30.540416925007207 0.013121403719342412 0.05039604031914888 1.0 True
17 3 4 0.5366157827594368 29.188000113022152 0.8855689764780674 0.07250679559229746 6 11.672391892657863 0.0006574625393083392 0.0008548453445728261 1.0 True
18 3 5 0.6760093701980526 47.53150989980517 0.8759311584895967 0.08543306637795112 6 15.073465050917047 0.0011073394938326227 0.004974962253885575 1.0 True
19 3 6 1.4361364806393455 80.09091141471853 0.678819891780469 0.10239967326707564 6 23.156860004682848 0.002691305620040523 0.027681404773136364 1.0 True
20 3 7 2.1008551524156123 104.37575791053635 0.7961101683853283 0.10180989325686585 6 29.756525470837225 0.023292005056525338 0.174639961337377 1.0 True
21 3 8 1.3108481779402037 133.20736428981294 0.11556480999479438 0.1433106939673773 6 147.35707874920627 1.368389528072986 15.0277123732284 1.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
22 4 5 0.17861948949299924 18.343509786783002 0.8685714285714285 0.07604325543622346 6 12.370184068840063 0.003904582263467037 0.011742002375313174 1.0 True
23 4 6 1.5807073315069207 50.90291130169636 0.6822060883963826 0.10567824422734254 6 18.163522769519293 0.011923922180697284 0.08008579261790838 1.0 True
24 4 7 1.7804201387535592 75.18775779751417 0.7963870967741935 0.10178391918503964 6 25.315987799570973 0.034377900701078094 0.11532260919614924 1.0 True
25 4 8 1.2818497736845669 104.01936417679076 0.7430850379518847 0.10189238984319506 6 25.19640114706588 0.034977663635135596 1.5753470469755222 1.0 False forward_reverse_rotation
26 4 9 1.5141865499370524 117.34615495378958 0.0841833440929632 0.15176056899798168 6 84.88516221869183 3.340127368239112 6.177861200193235 0.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
27 5 6 1.5316987521992935 32.55940151491337 0.6864384971693258 0.10315026388508003 6 11.505426214947052 0.002552272421554712 0.013899678900349944 1.0 True
28 5 7 1.6091514152462776 56.844248010731185 0.8104549602398644 0.09862238510016193 6 16.517299930207333 0.0037398303757535147 0.02129636862756259 1.0 True
29 5 8 1.1872485760158462 85.67585439000777 0.7806082661814401 0.10305777405801758 6 17.737819341361416 0.0056799616182010805 0.026182015145595202 1.0 True
30 5 9 1.380208837139288 99.00264516700663 0.7946777980693973 0.10313535699942489 6 14.147499646203187 0.06270036555847405 0.33857191756776234 1.0 True
31 5 10 1.7506315276318887 115.10127466723206 0.7847418443359877 0.10018431236036145 6 21.425526326745782 0.005291671170548772 0.044796841001461946 1.0 True
32 6 7 1.5692385974903325 24.284846495817817 0.7380672159016608 0.1046107504689679 6 13.947054238965462 0.010178220865611676 0.026255002379027764 1.0 True
33 6 8 0.5035712140385419 53.11645287509441 0.7026075619295958 0.10337241424878332 6 21.232848227388097 0.017447450845101884 0.1292664848366822 1.0 True
34 6 9 0.8508108569116883 66.44324365209327 0.6714210939544621 0.10949604778423808 6 34.21205543097956 0.09491078892598759 0.32255986251320673 1.0 False forward_reverse_translation
35 6 10 1.3331914459292633 82.5418731523187 0.6619427982478743 0.10510077922086615 6 31.101566437428897 0.003562383894956786 0.04536548719264196 1.0 True
36 6 11 3.16887584646763 119.46156331049859 0.638006230529595 0.1061575017783152 6 31.943306843588733 0.0059304520508679575 0.05963953472442965 1.0 True
37 7 8 1.1230406357307108 28.831606379276582 0.8040692297529396 0.09680052952350976 6 22.21980394474879 0.008328073875015064 0.06350723756646647 1.0 True
38 7 9 0.7186795671679719 42.158397156275456 0.8274764620076913 0.09296346258223728 6 22.096355228992984 0.0076981468910999155 0.020277033650542 1.0 True
39 7 10 0.39293571356254015 58.25702665650087 0.7992429186790236 0.10479299924479576 6 30.246845596558646 0.04240408437264065 1.1111803590641476 1.0 False forward_reverse_rotation
40 7 11 2.4544901278439815 95.17671681468076 0.7502523977788995 0.1028589368143411 6 19.931284665592216 0.005620519728387707 0.0303880139721275 1.0 True
41 7 12 3.0905023273107046 115.3786462164333 0.7054418372441338 0.10706084863640537 6 10.761985004268995 0.004629206980441498 0.07009059694702204 1.0 True
42 8 9 0.4407680716864947 13.326790776998866 0.8539132734003173 0.09158655960119419 6 31.292808872701826 0.0036696927581956673 0.014909075167693285 1.0 True
43 8 10 0.968166048306331 29.425420277224287 0.7989328474752733 0.09757506446655329 6 25.45934970841849 0.008689580645481346 0.052297435035790506 1.0 True
44 8 11 3.0540124804140354 66.34511043540417 0.6854158802063672 0.10755838259974337 6 42.46956117899474 0.00234148729464824 0.061662020462905004 1.0 True
45 8 12 3.652970846697932 86.5470398371567 0.6485376477909147 0.11031208403019 6 30.049664340525695 0.013830104055554564 0.11563630256710061 1.0 True
46 8 13 4.357531202056024 107.49623325505803 0.622879241516966 0.1149201207874094 6 27.04257772502661 0.005835943764379069 0.23887217550290574 1.0 True
47 9 10 0.5276649044820116 16.098629500225417 0.8486154649947754 0.09691837979566219 6 22.577834737399076 0.007052114296262031 0.2907164943632002 1.0 True
48 9 11 2.680593454060616 53.0183196584053 0.6963000378835712 0.10535088240045436 6 39.530571540955094 0.0034527207661208545 0.020238883727394828 1.0 True
49 9 12 3.2944321799753977 73.22024906015783 0.6584238791057825 0.10587631557960675 6 33.498316372288826 0.009482405807021465 0.07225186033919398 1.0 True
50 9 13 4.1147924441994315 94.16944247805917 0.621557336004006 0.11335203160746431 6 32.6975845279655 0.009011781776631502 0.0416830306778097 1.0 True
51 9 14 3.748520283907519 169.69584626488452 0.048890560361037984 0.15557197380465354 6 51.54107837707314 1.0864076801076206 7.184564913859941 0.5 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
52 10 11 2.249454986440387 36.91969015817989 0.7156362731683045 0.10422606491110974 6 45.45254669271086 0.004301999273657816 0.02361974788713562 1.0 True
53 10 12 2.8783779741216167 57.12161955993242 0.692875599852344 0.10691573133186853 6 39.11010182412513 0.004231594662123499 0.01872424915403812 1.0 True
54 10 13 3.84803303865234 78.07081297783375 0.6594721262950173 0.10871022234744057 6 43.1464607017966 0.006980776690426113 0.21408921607631748 1.0 True
55 10 14 3.3834992499807997 153.59721676465847 0.062245276028158575 0.15570919185507412 6 59.4104405367878 0.09444016841800375 2.8556589503073977 0.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
56 10 15 1.4957886147833104 171.72980583515965 0.6232518545542989 0.10969245891269987 6 43.00146365447724 3.0001452916434883 4.8522273147326915 1.0 False forward_reverse_translation;forward_reverse_rotation
57 11 12 0.636223282239137 20.20192940175253 0.8688915375446961 0.08660981428891965 6 18.033297623211222 0.005089530929434581 0.03170796903435614 1.0 True
58 11 13 2.0145868546459504 41.151122819653864 0.8021741727392188 0.10650327571539893 6 18.339724002801702 0.04485533499047456 0.27313934813876123 1.0 True
59 11 14 1.2405786051647103 116.67752660647822 0.7455741626794259 0.11063741423489376 6 20.694961222671232 0.007044503253911164 0.40985193451245056 1.0 True
60 11 15 1.4202907399288436 151.3505040066613 0.7466415272213057 0.10329430650455694 6 27.704214205277534 0.13203399207663835 1.4478079414864902 0.5 False forward_reverse_translation;forward_reverse_rotation
61 11 16 2.7368971716016928 176.81389445065594 0.78639603721155 0.10318237166131629 6 34.658693494341755 0.008767229441137649 0.032548654712657686 1.0 True
62 12 13 1.5928240921286525 20.949193417901323 0.8217283366828231 0.10684146627686454 6 16.400323597466677 0.01763325318199382 0.09467538550034012 1.0 True
63 12 14 0.6774809171975742 96.47559720472566 0.7611268939393939 0.10826784336909262 6 10.635638995146312 0.0015931090432955832 0.024585439481643892 1.0 True
64 12 15 1.8892292086221258 131.14857460490867 0.7324469325868906 0.11113399174419211 6 19.970471882560926 0.0024416100752998565 0.009279978214820864 1.0 True
65 12 16 3.349436875941103 156.6119650489076 0.7500298864315601 0.10525785108693746 6 30.70828038440084 0.0024719134275639418 0.013160987022490347 1.0 True
66 12 17 6.51378754358264 111.73754176984298 0.6198434030618207 0.1221546776649581 6 23.54160456185754 0.013538123192296372 0.06236055877935909 1.0 True
67 13 14 0.9721300146850552 75.52640378682433 0.8278562107011508 0.10919246415865995 6 12.723402368483741 0.0049872211162087675 0.02566897887278044 1.0 True
68 13 15 2.426469315960194 110.19938118700728 0.7886616014026885 0.10800229196760061 6 13.66454080002457 0.0038816141140735755 0.020030260506224723 1.0 True
69 13 16 4.157137927296689 135.66277163100625 0.7477833692786964 0.10901310110319071 6 21.034367079780026 0.0024107551433002806 0.01470679715840447 1.0 True
70 13 17 6.347445048137873 90.78834835194164 0.6122042632935114 0.12126385638135445 6 27.550293797111443 0.010111269848200304 1.6132660739833655 1.0 False forward_reverse_rotation
71 13 18 4.26917989710959 135.79234683500215 0.6756988719960765 0.1140499722068077 6 13.668694297489541 0.010188526275029454 0.05757819838859875 1.0 True
72 14 15 2.176233486689304 34.672977400182965 0.7415730337078652 0.11426627617931512 6 13.437209845730706 0.009280638111078932 0.07287904157175247 1.0 True
73 14 16 3.79947855245853 60.13636784418198 0.7130173965206958 0.11194096137848975 6 36.60672565292038 0.0004016408999195087 0.020284570842973915 1.0 True
74 14 17 6.190474035537447 15.261944565117341 0.6118212736015011 0.11991020962112661 6 17.152314443231333 0.020151321868834806 0.2079666531947748 1.0 True
75 14 18 4.170672674565236 60.26594304817776 0.6756292203806016 0.1138218459710723 6 11.041435170572107 0.023698120255922432 0.1321697884270417 1.0 True
76 14 19 3.9999134794649294 94.30913149027137 0.6699172941612147 0.112413086617321 6 9.748384934395327 0.018186549996561333 1.2493862686891326 1.0 False forward_reverse_rotation
77 15 16 1.7328875589135755 25.463390443999018 0.7714218177520388 0.10015132538295435 6 21.49868629780701 0.007439481433750378 0.036349268136368934 1.0 True
78 15 17 8.352264787019024 19.411032835065622 0.5587657459840518 0.12832496127520593 6 38.53228682833441 0.005430301274456062 0.06530638270069017 1.0 True
79 15 18 6.346037230040679 25.592965647994788 0.615040502962157 0.11840729454457544 6 21.032065383701333 0.012871328625232324 0.037753566213482644 1.0 True
80 15 19 5.607512178689573 59.636154090088375 0.6234350309955026 0.11735435665832353 6 16.270394372537467 0.007833445352022574 0.016491686030251827 1.0 True
81 15 20 8.633248754322691 149.96154617253603 0.5562658304185261 0.13192141621005607 6 26.944324835385952 0.023510650105734523 0.673469393155776 1.0 False forward_reverse_rotation
82 16 17 9.849975560032544 44.87442327906463 0.5900047370914259 0.1265936504427481 6 90.52736834449948 0.011296955150750666 0.21501790849158783 1.0 True
83 16 18 7.9099649541476005 0.12957520399577063 0.6471098982882659 0.12019779875781794 6 38.44752656622707 0.016821400599454826 0.06522344447557246 1.0 True
84 16 19 6.609737623005951 34.172763646089344 0.6280868046894488 0.12242728727104996 6 33.04590657073916 0.06745241310870236 1.6183598806956783 1.0 False forward_reverse_rotation
85 16 20 9.541288639020653 124.49815572853673 0.5791883197228409 0.127436475456937 6 40.945151692592816 2.599521231663455 5.87323656621683 1.0 False forward_reverse_translation;forward_reverse_rotation
86 16 21 9.407850320517097 175.38299577015903 0.49424730531670097 0.13389808634231865 6 45.28912477989511 0.04478742358365585 0.10475809279093058 1.0 True
87 17 18 2.081596655954183 45.003998483060414 0.7629028349890962 0.10358638519274684 6 18.62989302843653 0.007136146106563684 0.13887814859043496 1.0 True
88 17 19 4.34928579963815 79.047186925154 0.6728380024360536 0.10727518915463004 6 33.81541258417489 0.0017207670327870018 0.035486867252565536 1.0 True
89 17 20 4.4206846692505035 169.37257900760153 0.6354846507130771 0.11168219002675635 6 38.40506040461897 0.020290214498459216 0.2831192364677464 1.0 True
90 17 21 6.400116212709096 139.7425809507742 0.5695348561959995 0.1231813524465837 6 57.78224861663297 0.009859208985419086 0.31053558178227947 1.0 True
91 17 22 8.798226453379533 96.08482130259475 0.5459048079246195 0.1287223998113377 6 62.758312260043155 0.03194615937469955 0.5773515871730924 1.0 False forward_reverse_rotation
92 18 19 3.459222336215112 34.04318844209358 0.7562595809913132 0.11139160446923307 6 9.605522638453952 0.013156152321531143 0.09121207325672914 1.0 True
93 18 20 4.94806857498194 124.36858052454107 0.6668778509883426 0.1149564835181103 6 18.76442873201715 0.0076759006397633354 0.11713903044228172 1.0 True
94 18 21 6.5847443121227816 175.25342056617163 0.5977438948803768 0.12625086545570646 6 26.666713369082085 0.021119392446408227 0.18202979634982344 1.0 True
95 18 22 9.20681259274594 141.08881978565523 0.5286220871327254 0.13561310095446666 6 65.22594013516775 0.03713510940704989 0.6483189610135184 1.0 False forward_reverse_rotation
96 18 23 13.164275822166362 109.14103513098563 0.47146496815286626 0.14168298172295804 6 112.91469628781756 0.01208598984738065 0.6082838532521024 1.0 False forward_reverse_rotation
97 19 20 3.031758915138065 90.32539208244746 0.7409531090723751 0.11651827109675585 6 10.060869835729363 0.0051743650857167135 0.07263138471648796 1.0 True
98 19 21 3.732423384308353 141.21023212407198 0.6563629565000623 0.12291187901196955 6 15.339627619255575 0.0075358808329252175 0.06620678153945667 1.0 True
99 19 22 6.37255124464243 175.13200822775684 0.5891492613346918 0.12519472268444526 6 28.30213785955742 0.010212573357042046 0.11056129275235052 1.0 True
100 19 23 10.37094757900695 143.1842235730793 0.4984627209838586 0.13443006540917718 6 96.22322799212152 0.02229275506707497 0.2903847316754494 1.0 True
101 19 24 7.752290899896264 99.91483670708607 0.5732565579014716 0.1300803661721533 6 45.37964984018865 0.07078749161909308 0.6582469262123747 1.0 False forward_reverse_rotation
102 20 21 2.057726146362386 50.884840041624386 0.7808828984790405 0.11119787612354914 6 10.584324886965474 0.0031476308503252674 0.022487168529706673 1.0 True
103 20 22 4.388457127616061 94.54259968980386 0.6557604850934815 0.11898674179731801 6 16.883756700029306 0.008500396323601164 0.06463449106814506 1.0 True
104 20 23 8.256548639868363 126.4903843444735 0.5833545108005083 0.12863144503040289 6 48.7242741462686 0.013890482176493085 0.14514813799569015 1.0 True
105 20 24 5.797339988732605 169.75977121046614 0.6348521385962685 0.11831053318180502 6 31.671029215629257 0.00725649124030166 0.27346756317921195 1.0 True
106 20 25 3.9584300095978864 99.81821396908215 0.7094296865164296 0.11461231093244224 6 16.207076145397426 0.005999722474434507 0.07369423294061743 1.0 True
107 21 22 2.665930009787955 43.65775964817948 0.7387220368310469 0.11520069835112393 6 16.025252253059314 0.0021484382611444506 0.09312757978022868 1.0 True
108 21 23 6.670349114267683 75.60554430284907 0.6238657551274084 0.11987801174807478 6 32.271874238810035 0.007927242611182222 0.08795608522524892 1.0 True
109 21 24 4.0761181240750135 118.87493116884215 0.6712192699279861 0.11172772070549726 6 21.556821129992922 0.04673963175312233 0.22273623714194987 1.0 True
110 21 25 2.626640779558244 150.70305401070652 0.699310174919931 0.11234398811200015 6 15.335715959290205 0.018678960705618183 0.1734016933410122 1.0 True
111 21 26 2.086425835169367 83.91641671720558 0.7166481550043194 0.11091971646336789 6 12.109452825928004 0.00977162867106615 0.0631934229127065 1.0 True
112 22 23 4.005228418650323 31.947784654669576 0.7252984505969012 0.11644876240570266 6 17.935878140214236 0.013002087545984397 0.11470841251550605 1.0 True
113 22 24 1.4231800329066895 75.21717152066267 0.7555499175440822 0.1089594430160029 6 14.427403734239578 0.0044919719825295985 0.008669004448985662 1.0 True
114 22 25 0.9824487562406952 165.63918634111403 0.7088209387190134 0.11165869814478939 6 23.409617344710625 0.005265657588642687 0.10195131255569051 1.0 True
115 22 26 3.0743977770984916 127.57417636538506 0.6831820474029249 0.11509847268437542 6 18.05320621935197 0.010313466929779696 0.02189018950989812 1.0 True
116 22 27 3.054692363582515 96.43768805753649 0.712325317889966 0.11297739003609018 6 14.570486778570737 0.006473104665270118 0.07627008321159866 1.0 True
117 23 24 2.6253440212817054 43.26938686599312 0.787546254944494 0.11112536909525574 6 13.667766137918514 0.003984385308883342 0.012875657690538275 1.0 True
118 23 25 4.30404511575867 133.69140168644446 0.6839640551828883 0.11561487899989795 6 40.22316701306096 0.009663940236368565 0.2587163051792124 1.0 True
119 23 26 6.580252552934289 159.52196102005493 0.6214811057570377 0.12356096223549605 6 59.18789907295204 0.03346639663911815 0.040359288659068966 1.0 True
120 23 27 6.261292051742304 128.3854727122061 0.6439154109155375 0.11883308044324468 6 48.252953818047224 0.01363202571969651 0.268813724284828 1.0 True
121 23 28 10.83543880539923 37.57580831192121 0.5551750380517504 0.13104613123848732 6 44.00847715074386 0.01592501575703795 0.1925228188054137 1.0 True
122 24 25 2.0155105379126432 90.4220148204513 0.7610619469026548 0.10446971824743242 6 22.29894236104597 0.01721789520038266 0.09266069416139829 1.0 True
123 24 26 4.344602404326578 157.20865211395216 0.6986700443318556 0.11795525471114342 6 30.354013262701024 0.0239438468814135 0.2700005297192175 1.0 True
124 24 27 4.194437499675352 171.65485957819877 0.7233746521629142 0.11505806966234582 6 24.218829066684407 0.0071603040929797065 0.1144565168286882 1.0 True
125 24 28 8.235813904401468 80.84519517791432 0.618522741669834 0.1218895746871147 6 34.55835443937473 0.008956300116613691 0.24319664279778394 1.0 True
126 24 29 2.7255497122534083 57.0551252080454 0.7635993899339095 0.11147922533242269 6 11.572525725181164 0.02246832420061709 0.4519372330281508 1.0 True
127 25 26 2.340922525153354 66.78663729350096 0.8176906646563639 0.1076748622762494 6 10.194320056407282 0.011727583881002021 0.04299213457608308 1.0 True
128 25 27 2.187150933555911 97.92312560134954 0.8508155583437892 0.10177690901936605 6 9.447407214356089 0.004921098494284399 0.04722324529000337 1.0 True
129 25 28 6.688406442287195 171.26720999836604 0.6552720874701521 0.1158307449607044 6 16.856702471098828 0.010027166778796438 0.069841684352646 1.0 True
130 25 29 1.411474020521721 147.47714002849685 0.7434442763489663 0.11227320270489505 6 19.207346965238134 0.005744598995027529 0.30413185830684 1.0 True
131 25 30 0.5324917816481396 118.09183853064393 0.7986111111111112 0.1061010900656728 6 14.995080416656407 0.004651476537899485 0.06337779258524685 1.0 True
132 26 27 0.6349061963265976 31.136488307848563 0.8835952231301069 0.10164781558403184 6 8.741658683232068 0.0010243697887392141 0.04630474119881922 1.0 True
133 26 28 5.008419104260455 121.94615270813352 0.7013346764039284 0.1156303654422143 6 10.39669408260953 0.016692399239017493 0.1408617368634082 1.0 True
134 26 29 2.224643201350574 145.73622267800252 0.7255273462170014 0.11343814488049828 6 14.171918272295303 0.0030631045126193056 0.10499072900350268 1.0 True
135 26 30 2.6736935848107626 175.1215241758588 0.7273877292852625 0.11178149175643984 6 20.18904245623208 0.002906139936114469 0.06343916895540856 1.0 True
136 26 31 6.867005915953627 145.82839665297058 0.6099962135554714 0.12348129729633385 6 43.35600938855713 0.011230201352754812 0.02547666380871279 1.0 True
137 27 28 5.622863551235285 90.80966440028486 0.7116811266188859 0.11387091842867321 6 10.816842555490693 0.011430300098438593 0.17858658887118406 1.0 True
138 27 29 2.458639091699311 114.59973437015381 0.7588294651866802 0.10898351788466304 6 11.452504275483305 0.002552411031283606 0.096053889700167 1.0 True
139 27 30 2.614522132166758 143.98503586800658 0.7699595755432036 0.10809432324457112 6 19.384788093934308 0.006419845852952349 0.09683669503892821 1.0 True
140 27 31 6.500901961576647 114.69190834512194 0.6407864885303756 0.12195597170279295 6 45.71099685307573 0.03203829198983217 0.21314212616538905 1.0 True
141 27 32 8.204840364723921 69.85846065460369 0.6120722798923491 0.12793570356778294 6 50.652407095458955 0.02476270066797521 0.18953242722457034 1.0 True
142 28 29 5.5254251571777075 23.790069969868927 0.6751737207833228 0.11816626627973714 6 15.58130572797597 0.0036062245918322845 0.04228106700822467 1.0 True
143 28 30 6.682895390289165 53.17537146772172 0.6476021763887132 0.12162303323930705 6 19.56745364015458 0.036760870155111036 0.061189777619461184 1.0 True
144 28 31 11.27338119162288 23.882243944837096 0.558515338972352 0.1324943729180927 6 46.74937334475135 0.011294835537302048 0.22869636591571527 1.0 True
145 28 32 12.874594603353637 20.951203745681177 0.5356867779204108 0.13793317141107478 6 47.36276875018484 0.009260784922164039 0.4736145985672847 1.0 True
146 28 33 13.796424177198949 59.567633422321585 0.49968659897204465 0.13776776698165452 6 59.65569689133837 0.03480709552965627 0.1328751318540718 1.0 True
147 29 30 1.1998189814142899 29.3853014978528 0.8126428027418127 0.11465616151168853 6 9.73882704495904 0.059123063905753705 0.35885997079417975 1.0 True
148 29 31 5.755712819539279 0.09217397496817598 0.7178318135764944 0.11831514282300001 6 19.572443598172608 0.03055388488044922 0.16788276176822323 1.0 True
149 29 32 7.3497160995478215 44.7412737155501 0.6866709594333548 0.12344589693451308 6 19.69654687402097 0.01847461637742171 0.10764991453418553 1.0 True
150 29 33 8.359102419083325 83.35770339219052 0.6151911468812877 0.12607024585552623 6 19.321252328369155 0.005640558537625966 0.060018922799470055 1.0 True
151 29 34 8.008340805017072 133.40261903862776 0.6489454636216149 0.1229782406905996 6 19.361211916752247 0.024951598645873194 0.20519725330906424 1.0 True
152 30 31 4.592128513798009 29.29312752288462 0.76103500761035 0.11418966560335815 6 16.756236396505788 0.010805499433410783 0.0764328342532002 1.0 True
153 30 32 6.2188831730504885 74.12657521340287 0.7051463949438926 0.11749249261595905 6 20.554466965332146 0.009422682577629085 0.037900377279079585 1.0 True
154 30 33 7.344850966128434 112.7430048900433 0.6064231738035264 0.12368925274094972 6 20.291143104650924 0.0147669225762263 0.09037435316445036 1.0 True
155 30 34 7.0347870411064095 162.78792053648075 0.6427946506686664 0.12352381867411776 6 21.47516866844496 3.1821548176867474 2.4099911828321603 1.0 False forward_reverse_translation;forward_reverse_rotation
156 30 35 9.281470813161304 166.02816439171798 0.690784364483562 0.12252550482763978 6 16.634587270964886 0.009824737708452339 0.14818916841264132 1.0 True
157 31 32 1.7043840283934533 44.83344769051828 0.7959078625659504 0.10965414855273443 6 12.882067962817517 0.009608345699366565 0.04073745185785107 1.0 True
158 31 33 3.3472002644811774 83.44987736715868 0.636089469716009 0.11810494837446742 6 13.701242758091343 0.01055590691460776 0.06271370554561609 1.0 True
159 31 34 3.311630046755356 133.49479301359594 0.6599326599326599 0.11513164519765848 6 15.199690471396346 0.002580333972310516 0.03476275200242535 1.0 True
160 31 35 4.805055361756335 164.67870808539848 0.7747933884297521 0.1099315236542533 6 13.983106845566315 0.002969158365974405 0.02482993261597952 1.0 True
161 31 36 5.921091773191984 169.88970913698103 0.79326799071447 0.11165900641583826 6 12.181844458808802 0.004151989866605975 0.032942374858786365 1.0 True
162 32 33 1.9394369188755196 38.61642967664041 0.7479238533282229 0.1140254726045761 6 12.862133545437658 0.005854383016193307 0.042356141718508426 1.0 True
163 32 34 2.1435446082722334 88.6613453230776 0.7426470588235294 0.11481351455228676 6 15.077486603426832 0.0025843844761028077 0.009064526838353553 1.0 True
164 32 35 3.1027737257249126 119.84526039487966 0.8209641402863523 0.10635864973089924 6 10.914008667831691 0.004447738370013908 0.11616784364821804 1.0 True
165 32 36 4.235311686227635 145.27684317250132 0.7613800341073068 0.10898930843612145 6 12.22686011069426 0.008302372052489307 0.047221847858387886 1.0 True
166 32 37 3.1931475050378357 118.78221388859481 0.6919923126201153 0.11497666348297753 6 16.91846428853567 0.00567166990415677 0.10190182649448634 1.0 True
167 33 34 0.5704890065278277 50.0449156464372 0.8247678018575851 0.10774292174748101 6 13.307399451848953 0.005218203824516851 0.054034410494683056 1.0 True
168 33 35 2.3843314966611664 81.22883071823928 0.7097435897435898 0.11216257584808116 6 12.920219167499932 0.0055472051152683725 0.06161504272529624 1.0 True
169 33 36 3.5605240220038255 106.66041349586102 0.62202304737516 0.11282494351568334 6 18.491230751066993 0.009207361872581905 0.07430583689659953 1.0 True
170 33 37 4.013080941901569 80.16578421195443 0.6328828828828829 0.12540757119118434 6 17.018187124475837 0.0036940550280262596 0.12927083795080543 1.0 True
171 34 35 2.9403442697824866 31.183915071802076 0.7146673451214858 0.11428688542425412 6 13.705016766266674 0.013436446539858737 0.05510481554154184 1.0 True
172 34 36 4.102710388907318 56.61549784942384 0.6465506288908652 0.11729490897001751 6 19.754334744118587 0.086056763787067 1.0836879727751445 1.0 False forward_reverse_translation;forward_reverse_rotation
173 34 37 3.6865711881345757 30.12086856551724 0.6899292189246243 0.11937420197560855 6 16.808577408839643 0.005083360744530967 0.10024940937419655 1.0 True
174 35 36 1.2045561534352058 25.431582777621774 0.8242496050552922 0.10907898456971946 6 9.660062538057792 0.0026096461304541156 0.07595057929078583 1.0 True
175 35 37 6.084062615492111 1.063046506284832 0.6924659295448702 0.12103303062396852 6 14.36240888115935 0.005914867863779407 0.06573359298031699 1.0 True
176 36 37 7.2781244258509386 26.494629283906598 0.6402157164869029 0.12413886977390769 6 20.08544982146282 0.008397869289519334 0.08479281990151419 1.0 True
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results/01_previous_two_batches/small_gicp/extrinsic_batch2.json
-974
View File
@@ -1,974 +0,0 @@
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results/01_previous_two_batches/small_gicp/extrinsic_batch2_refined.json
-604
View File
@@ -1,604 +0,0 @@
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},
{
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},
{
"pair_index": 76,
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},
{
"pair_index": 77,
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},
{
"pair_index": 78,
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"rotation_deg": 1.1597952394623392
},
{
"pair_index": 79,
"translation_m": 0.016850631999927004,
"rotation_deg": 0.7568718165305058
}
]
}
},
"ground": {
"planes": 38,
"body_origin_height_above_ground_m": 0.2335,
"formula": "d_lidar - (R_X n_lidar)^T t_X - body_height"
},
"linearized_one_sigma": {
"translation_m": [
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],
"rotation_deg": [
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],
"warning": "conditional local estimate; bootstrap is the primary stability check"
},
"weighted_jacobian_condition_number": 6.329566115808833,
"solver_multistart": {
"runs": 12,
"candidates_relative_to_best": [
{
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"rotation_deg": 2.202963019076451e-08
},
{
"cost": 354.6347165886241,
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"translation_m": 7.896781546182907e-10,
"rotation_deg": 1.0169266187599847e-07
},
{
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"translation_m": 8.217931179403876e-10,
"rotation_deg": 6.93412504585164e-08
},
{
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"translation_m": 3.469446951953614e-18,
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{
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{
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"translation_m": 2.260359361733417e-10,
"rotation_deg": 2.6418689746833837e-08
},
{
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"translation_m": 7.589395457725227e-10,
"rotation_deg": 4.91609016107675e-08
},
{
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"success": true,
"translation_m": 1.2750789560311927e-10,
"rotation_deg": 7.759775984980414e-10
},
{
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"translation_m": 7.805526908313695e-10,
"rotation_deg": 6.684488070039544e-08
},
{
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"translation_m": 7.763945275712574e-10,
"rotation_deg": 8.931061588988946e-08
},
{
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"translation_m": 9.383630927674663e-10,
"rotation_deg": 1.3000921411943172e-07
},
{
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"success": true,
"translation_m": 6.944401937454179e-10,
"rotation_deg": 7.954648404736881e-08
}
]
},
"bootstrap": {
"runs": 100,
"order": [
"x_m",
"y_m",
"z_m",
"roll_deg",
"pitch_deg",
"yaw_deg"
],
"std": [
0.0017068433472663032,
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],
"p025": [
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0.7193267956198541,
-0.9300082754299475,
1.0287033540941326,
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],
"p975": [
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1.254120304082275,
-0.8275267997610058
]
}
}
results/02_data4_calibration/README.md
-12
View File
@@ -1,12 +0,0 @@
# data4 独立标定结果
data4 含 34 个静止站点，LiDAR 来自逐站 dlog，RTK/IMU 来自独立 rscap。求解不使用手量外参初始化。
```text
translation_m = [1.300376020, -0.001706691, 0.704877045]
RPY_deg_xyz = [-0.791891617, 1.393823210, -0.970742631]
AX RMS = 0.11762 m / 1.24257 deg (26 pairs)
condition = 7.44293
```
该结果用于独立对照，不是当前部署值。`final_extrinsic_data4.json` 与 `consensus/extrinsic.json` 数值相同；前者是便于下游读取的顶层副本。
results/02_data4_calibration/common/ground_planes.csv
-35
View File
@@ -1,35 +0,0 @@
time,nx,ny,nz,d,inliers,rms_m,frame_counter
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1784784224.2408776,-0.02761369891036776,0.0016288328836576937,0.9996173420544108,0.9001377900125058,1799,0.012615520511386497,4371
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1784785506.389296,-0.03393096202185679,-0.007325597743377637,0.9993973311120926,0.9387396500642948,1844,0.01340053385502904,17193
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1784785967.8726046,-0.031038730802887403,-0.010699305087773895,0.9994609157244643,0.9511405539250901,1512,0.013166899954854165,21808
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1784786252.6621523,-0.040972893104105006,-0.02082299858725662,0.9989432540242288,0.9524735117369321,1736,0.012945834939507173,24656
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1 time nx ny nz d inliers rms_m frame_counter
2 1784783825.357129 -0.011572516208853837 -0.01546146293660183 0.9998134926237273 0.9428804652027359 2148 0.011933750988078078 382
3 1784783905.353819 0.0037183584307534687 -0.006818427483323246 0.9999698409738341 0.9412842800288904 1992 0.011942807895967073 1182
4 1784783971.0503054 -0.021709843154394736 -0.005232422217807239 0.999750621139065 0.9451429710867566 1863 0.012080615710528817 1839
5 1784784059.7468228 -0.02414670904491499 -0.0009406084776101358 0.9997079832120939 0.9517260475425733 1921 0.013468410314875789 2726
6 1784784149.2434597 -0.034005828386544125 0.003026503321489513 0.9994170520425345 0.9257219945735469 1673 0.012714432789206961 3621
7 1784784224.2408776 -0.02761369891036776 0.0016288328836576937 0.9996173420544108 0.9001377900125058 1799 0.012615520511386497 4371
8 1784784301.6372502 -0.0071762210837946405 -0.011511959356927467 0.9999079840878965 0.9580809447760092 2113 0.012007730376482893 5145
9 1784784387.733771 -0.010489658730106238 -0.009076163262051764 0.9999037905319524 0.9306898458653136 2187 0.01214615852280608 6006
10 1784784474.9314597 -0.016114135387570262 -0.0509432368134707 0.9985715403834008 0.9072229543504865 1894 0.012012086783846727 6878
11 1784784549.4274275 -0.025174259196536854 -0.013435577824823405 0.9995927880504234 0.9410339772836553 1603 0.01154746446935748 7623
12 1784784614.7244046 -0.03173300872144653 -0.01870285516152855 0.9993213794202 0.932566466561638 1970 0.01233410897082844 8276
13 1784784682.921899 -0.022665842512528424 -0.02998919824936144 0.9992932040054899 0.9145231002636922 1663 0.012948552270457988 8958
14 1784784758.8187964 -0.023688193326187944 -0.030852054660206355 0.9992432237549499 0.9349709939783197 1756 0.013443733500218538 9717
15 1784784836.0155501 -0.018461711654128285 -0.02498001140817507 0.9995174656967467 0.94720244509644 2057 0.013445893726732725 10489
16 1784784921.1126208 -0.01993595874096254 -0.02538449671402936 0.9994789566947642 0.9120981909462803 1822 0.012774262320504949 11340
17 1784784992.709947 -0.017778693539407146 -0.026803117327322167 0.9994826216386005 0.9369057170493221 2114 0.012253841210002203 12056
18 1784785067.6067727 -0.03766905893322114 -0.00624993226014667 0.9992707242513558 0.9491859078478477 1753 0.01368084681318558 12805
19 1784785215.9006598 -0.023255779098430147 -0.030526061005580876 0.9992633928739754 0.9173759052467025 2145 0.012710466955150847 14288
20 1784785296.4990919 -0.014034225033964978 -0.025014032112439084 0.9995885847313253 0.9275558675790446 2445 0.012495377814995792 15094
21 1784785363.1952267 -0.03352240146955635 -0.008711847039715113 0.9993999961581301 0.9543448006240765 1968 0.01391585588879859 15761
22 1784785434.592462 -0.016701702947054253 0.016663838961403298 0.9997216460544089 0.9289121671743757 1857 0.011181698281215124 16475
23 1784785506.389296 -0.03393096202185679 -0.007325597743377637 0.9993973311120926 0.9387396500642948 1844 0.01340053385502904 17193
24 1784785587.5863533 -0.03382019410288933 -0.0007820019558384217 0.9994276276668481 0.9255866795455666 1691 0.012723688174337812 18005
25 1784785681.9825997 -0.03422781555599595 -0.019255467037831406 0.999228544243718 0.9454924962027047 1959 0.01264678059788212 18949
26 1784785815.4779446 -0.0011902245774294716 -0.023262098479360813 0.9997286922659526 0.9523139768255908 2026 0.012446728838862407 20284
27 1784785891.9768085 -0.02758114176328463 0.0022252358991911286 0.9996170911625244 0.9417694614877323 1551 0.012616490996777729 21049
28 1784785967.8726046 -0.031038730802887403 -0.010699305087773895 0.9994609157244643 0.9511405539250901 1512 0.013166899954854165 21808
29 1784786031.5701303 -0.023257481598180085 -0.0214545061132305 0.9994992714940555 0.9380611442094842 1555 0.013339981386836522 22445
30 1784786087.9670725 -0.02720411594210121 -0.012536181034248165 0.9995512894498637 0.942804167293392 1531 0.013246549575843425 23009
31 1784786160.8647907 -0.030839502708575457 0.06473269778477829 0.9974259886879789 1.0783938869067702 1436 0.010501132985089602 23738
32 1784786252.6621523 -0.040972893104105006 -0.02082299858725662 0.9989432540242288 0.9524735117369321 1736 0.012945834939507173 24656
33 1784786319.6581354 -0.029761355495275914 -0.026565272466750618 0.9992039571668294 0.9594371422885729 1571 0.013108184070957877 25326
34 1784786396.7558627 -0.030769194030483185 -0.022645428394942407 0.9992699541522921 0.9756606317668476 1326 0.013202989492489907 26097
35 1784786557.4492514 -0.019983269314458783 -0.016965365794746882 0.9996563636124948 0.9409853928234136 1228 0.013269243263785414 27704
results/02_data4_calibration/consensus/B_consensus.consensus.json
-340
View File
@@ -1,340 +0,0 @@
{
"selection_is_X_independent": true,
"B_source": "Open3D; small_gicp is used only as an agreement gate",
"max_translation_m": 0.05,
"max_rotation_deg": 0.5,
"input_open3d_pairs": 42,
"accepted_pairs": 26,
"pairs": [
{
"i": 0,
"j": 1,
"open3d_small_translation_m": 0.014276441704401843,
"open3d_small_rotation_deg": 0.61360593819684,
"accepted": false,
"reason": "backend_disagreement"
},
{
"i": 0,
"j": 2,
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"open3d_small_rotation_deg": 0.1460927002571647,
"accepted": true,
"reason": ""
},
{
"i": 1,
"j": 2,
"accepted": false,
"reason": "not_in_small_gicp_refined"
},
{
"i": 2,
"j": 3,
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"accepted": true,
"reason": ""
},
{
"i": 2,
"j": 5,
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},
{
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},
{
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"j": 6,
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"reason": "backend_disagreement"
},
{
"i": 5,
"j": 8,
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},
{
"i": 6,
"j": 7,
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},
{
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},
{
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},
{
"i": 10,
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{
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{
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},
{
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},
{
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},
{
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"reason": ""
},
{
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},
{
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},
{
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"reason": ""
},
{
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results/02_data4_calibration/consensus/B_consensus.npz
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results/02_data4_calibration/consensus/extrinsic.json
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0.0052124106362270465,
0.002294080818689292,
0.09999053340933307,
0.10013087210161215,
0.1488688877903013
],
"p025": [
1.2918362278137754,
-0.009531330977586527,
0.7011199682224338,
-0.9599898042168808,
1.2066688822355272,
-1.2617485460367455
],
"p975": [
1.3082553293462966,
0.009443875945124016,
0.7092373306937458,
-0.5988363907444886,
1.5590218787244785,
-0.7051603064062103
]
}
}
results/02_data4_calibration/open3d_gicp/B_estimation.npz
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results/02_data4_calibration/open3d_gicp/B_quality.csv
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@@ -1,97 +0,0 @@
i,j,rtk_translation_m,rtk_rotation_deg,heldout_inlier_ratio,heldout_inlier_rmse_m,hessian_rank,hessian_condition,reverse_translation_m,reverse_rotation_deg,multistart_success_rate,accepted,rejection_reasons
0,1,1.8477087194158957,24.171297449440786,0.8061657032755298,0.10961296014103376,6,2.7038608113687213,0.004225163540003135,0.15304490677206398,1.0,True,
0,2,2.633731568575307,80.09074797031298,0.7489394523717702,0.116305716193008,6,3.0720058333957385,0.02073003109723772,0.12418344306820694,1.0,True,
0,3,6.923255970100826,79.91583883299243,0.6310283235519265,0.12470979645173065,6,5.235632990817995,0.02154775870989581,0.2539904497958252,1.0,True,
1,2,1.7155362084417105,55.91945052087219,0.7867383512544803,0.11079021393934936,6,3.282529873989152,0.0076823267394430066,0.05080433004685343,1.0,True,
1,3,5.885389341942907,55.74454138355163,0.6794562317367552,0.1190716913809658,6,4.183386002322129,0.024832426027852336,0.29364033194785566,1.0,True,
1,4,2.3031736807956613,106.08652205569953,0.6786112833230006,0.11255015823152649,6,3.2941312581877575,0.007769137931169012,0.07270245476793177,1.0,True,
2,3,4.339151683168145,0.17490913732054883,0.7586776859504132,0.11541610277862563,6,3.730803369806123,0.007879904085793275,0.11659483159946175,1.0,True,
2,4,0.6150408096900305,50.167071534827386,0.7854572527608884,0.1098649389241601,6,2.641287751481569,0.017042953274278242,0.09383247792992147,1.0,True,
2,5,5.894923525501676,8.735318060700383,0.7074574574574575,0.11912871456224478,6,3.8527093190832473,0.016641241123247028,0.24777402858141082,1.0,True,
3,4,3.9735035126146885,50.34198067214791,0.6974624291697462,0.1172763088894914,6,3.5010457716923225,0.012148721219467648,0.2079308684382429,1.0,True,
3,5,2.3619393178889707,8.910227198020932,0.7962985964476462,0.10646082199215777,6,3.0377458538379902,0.011008298723511818,0.08217077610411756,1.0,True,
3,6,2.2300116834828536,47.63555775102664,0.8376509054325956,0.10956583416752592,6,3.1930663579156233,0.001222821038316072,0.09207521511761255,1.0,True,
4,5,5.370771070097231,41.431753474126985,0.6652516676773802,0.12322094568314959,6,4.985227704290952,0.005399786589867114,0.1389350777589359,1.0,True,
4,6,6.142548765456278,97.97753842317456,0.6529585072428186,0.11878586101480827,6,5.324803753668097,0.011377811219747914,0.20418431358287809,1.0,True,
4,7,5.314106070657687,123.28910472998356,0.6964418087472202,0.11691192595448072,6,4.414726450762486,0.017641796253111963,0.10886712169052283,1.0,True,
5,6,1.9357867378988893,56.54578494904757,0.7526921648718901,0.10924852668609378,6,3.289802074469556,0.012374747586501101,0.14461856068326442,1.0,True,
5,7,0.19501559913675365,81.85735125585654,0.7833561729164071,0.11683079277948674,6,2.8807765869032655,0.0136898748124479,0.2000656277647698,1.0,True,
5,8,1.8038807059856978,172.47951556359365,0.7410703250525275,0.11436945722491815,6,3.9768956884456648,0.020336999081352437,0.07548452187379719,1.0,True,
6,7,2.130456053070114,25.311566306808988,0.8497729566094854,0.10415909908074772,6,3.375936129784756,0.008986805974948919,0.1309947950641294,1.0,True,
6,8,0.24494500622098103,115.93373061454483,0.7678928928928929,0.11166725079781287,6,3.2609205733873607,0.010085391730565987,0.11752836996157842,1.0,True,
6,9,8.025985916230132,103.90638727582186,0.6071384156199477,0.12594282886521954,6,6.882498483502561,0.01655440444551025,0.4345945520890901,1.0,True,
7,8,1.9962664218365056,90.62216430773583,0.8299748110831234,0.10748525688830209,6,3.0071179226782414,0.007106461978852689,0.10698728473689886,1.0,True,
7,9,7.8811064994361235,78.59482096901284,0.6188509200150206,0.1271320507839345,6,6.716979637303383,0.026230622734929154,0.28112746782878123,1.0,True,
7,10,7.620696678413973,134.60031955305035,0.5757088027733069,0.1313446077529101,6,5.755535843660075,0.012710332356906473,0.2297418063823675,1.0,True,
8,9,7.803713858547152,12.027343338722998,0.6326834719980131,0.12084265385763356,6,5.539689880493177,0.02518064403521875,0.3859647522318924,1.0,True,
8,10,8.062504137991457,43.97815524531457,0.6163861933423412,0.12936028463965984,6,4.789005902863088,0.011327540721525892,0.17059480981566058,1.0,True,
8,11,11.084531710563947,22.606708130954026,0.5371195721380364,0.13463285672049757,6,5.833003178860405,0.017887959781482814,0.14919899299178907,1.0,True,
9,10,2.04815062353057,56.00549858403758,0.6543345543345543,0.10729272360686735,6,3.3788563349731584,0.008725655639009402,0.02870462611907292,1.0,True,
9,11,4.738677237611319,34.634051469677026,0.5818780055682106,0.11717687815100752,6,3.555986532172074,0.009813025835682346,0.0603546019895227,1.0,True,
9,12,7.170741483679294,16.35622721712263,0.5379123584441162,0.12645695585785732,6,4.882558096197038,0.008862930161052695,0.11566409877698863,1.0,True,
10,11,3.2047552083250137,21.371447114360556,0.7118898623279099,0.1192865608074921,6,3.2202034715576238,0.0029529340190147615,0.0041769080934441144,1.0,True,
10,12,6.291813977735496,39.64927136691496,0.6120311738918656,0.1252565212812615,6,4.691686416856199,0.005686910809265337,0.10204044727301474,1.0,True,
10,13,10.199392557022867,72.41150002956134,0.516551290119572,0.1354903305714049,6,7.346543684414892,0.01356504272158301,0.3255230434099281,1.0,True,
11,12,3.467398797536633,18.277824252554396,0.650555275113579,0.12104882943540958,6,4.612247786063486,0.003583199374499245,0.03300207707174736,1.0,True,
11,13,7.516502113110916,51.04005291520078,0.5698054068172914,0.12858662743221627,6,7.539783468898054,0.016001435752891854,0.11059625579949509,1.0,True,
11,14,3.767517331528496,20.548768889074672,0.6420881321982974,0.12414062948335584,6,4.9522650472668115,0.012369101516230236,0.018705060406060074,1.0,True,
12,13,4.049286119591895,32.762228662646386,0.6972966112450819,0.11680896213116294,6,4.282247993741361,0.011410238767832601,0.04779713993430858,1.0,True,
12,14,0.97948616772873,2.2709446365202806,0.8749086479902558,0.09521299965540617,6,3.309695139564419,0.006159472215773642,0.014929948455572307,1.0,True,
12,15,4.286747470271891,25.863710300929224,0.7022030893897189,0.11797995277580095,6,4.2772327867721325,0.008495008365045943,0.102782991447036,1.0,True,
13,14,4.006260191078547,30.491284026126113,0.6955810147299509,0.1145595612270532,6,3.350289886810732,0.010228664633443074,0.03515966054944097,1.0,True,
13,15,0.9562774815922267,6.898518361717157,0.868300353819945,0.10454213568084784,6,3.243735713395398,0.0023750253827712867,0.010725047644197173,1.0,True,
13,16,3.565173336606111,18.944899794614482,0.7265456392027422,0.10962529664062398,6,3.522751424445623,0.008958927594995584,0.0304143851242741,1.0,True,
14,15,4.019575892829469,23.592765664408944,0.7120070334086913,0.11868441290330693,6,4.620592469502459,0.002571958018982041,0.05506919751152759,1.0,True,
14,16,7.5676649485439835,49.43618382074059,0.5918615984405458,0.12229328437386527,6,7.149509813179243,0.014273957859022303,0.25325650727956367,1.0,True,
14,17,5.910977627463022,0.8461207481731591,0.6694009445687298,0.12443900216431929,6,5.157741429696001,0.017895201000461415,0.10920228290609475,1.0,True,
15,16,3.7301261399251735,25.84341815633164,0.702887537993921,0.11495230769293868,6,3.540289976352534,0.013545291843393993,0.033466251783377816,1.0,True,
15,17,2.2049738368271745,24.438886412582093,0.7429531936901991,0.11679524427533879,6,3.526664394280145,0.00989411002791081,0.07786907370564648,1.0,True,
15,18,4.7000039832559155,3.452521908779401,0.7209645010046886,0.11716134583909153,6,4.125231895423432,0.011654729311847106,0.13683586564190353,1.0,True,
16,17,3.368526196086246,50.282304568913744,0.618922305764411,0.11254196340939995,6,4.068632188828396,0.03104786021350874,0.10375098145235381,1.0,True,
16,18,3.5240348999326185,22.39089624755224,0.6890156918687589,0.11084024896736888,6,4.42116710884217,0.01556225520371614,0.02495881796886513,1.0,True,
16,19,2.146829294717985,30.035090485266103,0.8685060899826,0.10135543575024519,6,2.92007223300188,0.002952251383831446,0.02753369989560042,1.0,True,
17,18,2.640403049812329,27.891408321361506,0.7697708305735859,0.10648049893472207,6,3.7928223564531667,0.010582276181446382,0.039599051949106026,1.0,True,
17,19,3.933985934417215,80.31739505417984,0.6293759512937596,0.1095449771750205,6,3.6483069293931876,0.012240937390583118,0.060306184678878015,1.0,True,
17,20,4.2254212089887,152.98392843416656,0.6014520938674964,0.12300562605352797,6,4.719447385686107,1.3272837904090529,0.4474442011368189,1.0,False,forward_reverse_translation
18,19,2.4460967931915643,52.425986732818345,0.6827314510833881,0.10834806615100012,6,3.5073857685652805,0.012418496053917759,0.11905018881098527,1.0,True,
18,20,6.593931011285688,125.09252011280485,0.5756313809779688,0.1265759478434112,6,5.389095141151797,0.012917057356045326,0.24042373011343365,1.0,True,
18,21,11.793089868757727,175.70238585456048,0.4044519656339495,0.14396876383759685,6,10.041635341774429,0.06610149517267662,0.6118050873537593,1.0,False,forward_reverse_rotation
19,20,6.650720121741557,72.6665333799865,0.6234734541714874,0.12775305804057488,6,5.958603794025073,0.009237066767190358,0.22540488888099894,1.0,True,
19,21,12.053580264031138,123.27639912174077,0.3788200074840963,0.14687803396129923,6,10.170069582593054,0.1645149893124099,1.1204113986936788,1.0,False,forward_reverse_translation;forward_reverse_rotation
19,22,14.836246959975925,128.85294276465592,0.3215252152521525,0.1505833656059155,6,15.25410245430046,0.08605091089946505,0.48945936193054473,1.0,False,heldout_inlier_ratio;forward_reverse_translation
20,21,5.40286046809103,50.60986574175429,0.6596992097884272,0.12147955429086157,6,4.0820327359553845,0.014725537493637062,0.1988422472286775,1.0,True,
20,22,8.200956565000565,56.18640938466938,0.5739414499308958,0.13255415786946786,6,5.328486341352201,0.006040617548520927,0.19706632354687328,1.0,True,
20,23,5.175118275082073,70.79178325235415,0.6456945156330087,0.12075756038041646,6,3.9642702950866386,0.022949011013485506,0.1911210947924365,1.0,True,
21,22,2.8644847627909416,5.57654364291509,0.7716237647919971,0.11632271358541554,6,2.79620153032919,0.00996357879827298,0.15332892195322273,1.0,True,
21,23,1.2936223973175418,20.18191751059984,0.8576224819696593,0.09865676260631216,6,3.0367950693845156,0.0039225733250952055,0.00898487649368532,1.0,True,
21,24,2.3128500583741403,54.59467208208593,0.7715940569126165,0.1136150455355286,6,2.76815824933335,0.007475673605589086,0.042277690012993266,1.0,True,
22,23,3.7194009250537223,14.605373867684753,0.7396689147762109,0.11702962848624102,6,3.4148047250889095,0.01814015943430581,0.11847321816106485,1.0,True,
22,24,4.786117710081478,49.01812843917083,0.6983240223463687,0.11898873157361621,6,3.6334655936865663,0.013438931050857202,0.08244161259412694,1.0,True,
22,25,2.3747421598149763,12.391903814042255,0.736861094407697,0.11586394710110075,6,2.383007054117407,0.018559552524775386,0.0645260279790244,1.0,True,
23,24,1.0880644725336985,34.41275457148609,0.7853164556962026,0.1128725442321241,6,2.409921828847164,0.002195759849115173,0.032112959181416705,1.0,True,
23,25,5.033935954106079,2.213470053642494,0.6881127450980392,0.12303206700403986,6,2.9363727218037994,0.004939188019100004,0.12964064637100806,1.0,True,
23,26,5.765534437288107,40.730927532824346,0.6852618757612667,0.12040544972155913,6,3.0590065093977192,0.006032445250250899,0.14039335223024835,1.0,True,
24,25,6.097212810998921,36.62622462512857,0.677667493796526,0.12346315805814134,6,3.6286524357748307,0.02347922689116974,0.16273859629352566,1.0,True,
24,26,6.852918693116795,6.318172961338249,0.6530209617755857,0.12710147612984257,6,3.5331859775372005,0.013311199903818141,0.18193579850152686,1.0,True,
24,27,7.47982907414204,31.60254826458195,0.6649014778325123,0.1248115961442785,6,4.10006355979974,0.017872821205486625,0.1680556511699705,1.0,True,
25,26,1.2882746074869595,42.944397586466835,0.9127837514934289,0.0958142916589384,6,2.9017658682436953,0.003534148840172682,0.018440169300173608,1.0,True,
25,27,1.7666235781584831,68.22877288971053,0.8510739856801909,0.10418150333394147,6,2.368253417089997,0.006103232696002816,0.13366555345652814,1.0,True,
25,28,2.1807182972588706,88.09106909546726,0.8853518429870751,0.10761251229652005,6,2.6372860976794645,0.003483231665912094,0.0369774420199415,1.0,True,
26,27,0.6269948654714946,25.284375303243706,0.9183867141162515,0.09074909570650921,6,2.8142315359282506,0.00040199505815422204,0.011554314408129918,1.0,True,
26,28,0.9431372257108486,45.14667150900044,0.8853200095170116,0.10604184939655073,6,2.656402027789519,0.007320240476189879,0.12930335868605478,1.0,True,
26,29,1.129925637779203,87.41573662125148,0.7880466815984911,0.10871274240898265,6,3.111957466886604,0.0063952430610542755,0.039517035902874385,1.0,True,
27,28,0.44809323479571145,19.862296205756735,0.9289448669201521,0.0850509551532673,6,2.96325286846982,0.005157655135591474,0.022666807877311387,1.0,True,
27,29,1.160064136065596,62.13136131800778,0.7872365477452019,0.10455513464835667,6,2.9621627623005296,0.011650639607012138,0.1624719134077532,1.0,True,
27,30,4.25412615059095,152.09392556214777,0.04792444029850746,0.15706827084964883,6,3.4115087753552786,2.5641513880069633,2.874944882076104,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
28,29,1.5995723610319212,42.26906511225104,0.8000944621560987,0.1086547547358129,6,3.1466224100058553,0.0011394164967304414,0.02119627748719656,1.0,True,
28,30,4.295353381127667,132.23162935639104,0.042095416276894296,0.16025490727880456,6,4.631028587407634,2.5607552487652696,2.17230408479254,0.0,False,heldout_inlier_ratio;heldout_inlier_rmse;forward_reverse_translation;forward_reverse_rotation;multistart_instability
28,31,5.754426930327516,158.8421298011293,0.7465330381074466,0.1066966653439246,6,2.402598366822474,0.00635764905956918,0.040016615214377715,1.0,True,
29,30,4.099530130205247,89.96256424413995,0.7248812145092132,0.10870831469083347,6,2.9471223804737057,0.00465638537716095,0.12465763189227934,1.0,True,
29,31,4.890018768530818,116.57306468887772,0.7243012243012243,0.11138751941762699,6,2.7645021322697017,0.004263906321896464,0.046637396086304225,1.0,True,
29,32,4.464737187763289,159.39493219688632,0.6956070563818748,0.1103354081949904,6,3.227012588611917,0.011088061861347823,0.34561739459504637,1.0,True,
30,31,2.3862917603439455,26.61050044473775,0.8185562292643862,0.0961579573295129,6,2.899558208444643,0.004999163455591531,0.0213847958734735,1.0,True,
30,32,1.1507634071714652,69.43236795274659,0.8041343079031521,0.099533431536842,6,2.6146168742247573,0.003171376495143,0.02376737774843464,1.0,True,
30,33,2.4361832484951105,160.7145717128102,0.0824534345711235,0.13003518012314966,6,7.2913036106936,1.3743823383381788,1.9837113244583153,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
31,32,1.2514382602180638,42.82186750800885,0.8157085941946499,0.09925944770258267,6,3.0485411400768245,0.004719596234887584,0.06146741991681224,1.0,True,
31,33,0.641507519046697,134.10407126807203,0.0911563017261764,0.12702324561674966,6,7.541483837366941,2.816294381538612,3.333105317420589,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
32,33,1.4370687242949811,91.2822037600631,0.7569928006609229,0.10008740880870744,6,3.6611044282667207,2.988193350509204,2.696443494598379,1.0,False,forward_reverse_translation;forward_reverse_rotation
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 1.8477087194158957 24.171297449440786 0.8061657032755298 0.10961296014103376 6 2.7038608113687213 0.004225163540003135 0.15304490677206398 1.0 True
3 0 2 2.633731568575307 80.09074797031298 0.7489394523717702 0.116305716193008 6 3.0720058333957385 0.02073003109723772 0.12418344306820694 1.0 True
4 0 3 6.923255970100826 79.91583883299243 0.6310283235519265 0.12470979645173065 6 5.235632990817995 0.02154775870989581 0.2539904497958252 1.0 True
5 1 2 1.7155362084417105 55.91945052087219 0.7867383512544803 0.11079021393934936 6 3.282529873989152 0.0076823267394430066 0.05080433004685343 1.0 True
6 1 3 5.885389341942907 55.74454138355163 0.6794562317367552 0.1190716913809658 6 4.183386002322129 0.024832426027852336 0.29364033194785566 1.0 True
7 1 4 2.3031736807956613 106.08652205569953 0.6786112833230006 0.11255015823152649 6 3.2941312581877575 0.007769137931169012 0.07270245476793177 1.0 True
8 2 3 4.339151683168145 0.17490913732054883 0.7586776859504132 0.11541610277862563 6 3.730803369806123 0.007879904085793275 0.11659483159946175 1.0 True
9 2 4 0.6150408096900305 50.167071534827386 0.7854572527608884 0.1098649389241601 6 2.641287751481569 0.017042953274278242 0.09383247792992147 1.0 True
10 2 5 5.894923525501676 8.735318060700383 0.7074574574574575 0.11912871456224478 6 3.8527093190832473 0.016641241123247028 0.24777402858141082 1.0 True
11 3 4 3.9735035126146885 50.34198067214791 0.6974624291697462 0.1172763088894914 6 3.5010457716923225 0.012148721219467648 0.2079308684382429 1.0 True
12 3 5 2.3619393178889707 8.910227198020932 0.7962985964476462 0.10646082199215777 6 3.0377458538379902 0.011008298723511818 0.08217077610411756 1.0 True
13 3 6 2.2300116834828536 47.63555775102664 0.8376509054325956 0.10956583416752592 6 3.1930663579156233 0.001222821038316072 0.09207521511761255 1.0 True
14 4 5 5.370771070097231 41.431753474126985 0.6652516676773802 0.12322094568314959 6 4.985227704290952 0.005399786589867114 0.1389350777589359 1.0 True
15 4 6 6.142548765456278 97.97753842317456 0.6529585072428186 0.11878586101480827 6 5.324803753668097 0.011377811219747914 0.20418431358287809 1.0 True
16 4 7 5.314106070657687 123.28910472998356 0.6964418087472202 0.11691192595448072 6 4.414726450762486 0.017641796253111963 0.10886712169052283 1.0 True
17 5 6 1.9357867378988893 56.54578494904757 0.7526921648718901 0.10924852668609378 6 3.289802074469556 0.012374747586501101 0.14461856068326442 1.0 True
18 5 7 0.19501559913675365 81.85735125585654 0.7833561729164071 0.11683079277948674 6 2.8807765869032655 0.0136898748124479 0.2000656277647698 1.0 True
19 5 8 1.8038807059856978 172.47951556359365 0.7410703250525275 0.11436945722491815 6 3.9768956884456648 0.020336999081352437 0.07548452187379719 1.0 True
20 6 7 2.130456053070114 25.311566306808988 0.8497729566094854 0.10415909908074772 6 3.375936129784756 0.008986805974948919 0.1309947950641294 1.0 True
21 6 8 0.24494500622098103 115.93373061454483 0.7678928928928929 0.11166725079781287 6 3.2609205733873607 0.010085391730565987 0.11752836996157842 1.0 True
22 6 9 8.025985916230132 103.90638727582186 0.6071384156199477 0.12594282886521954 6 6.882498483502561 0.01655440444551025 0.4345945520890901 1.0 True
23 7 8 1.9962664218365056 90.62216430773583 0.8299748110831234 0.10748525688830209 6 3.0071179226782414 0.007106461978852689 0.10698728473689886 1.0 True
24 7 9 7.8811064994361235 78.59482096901284 0.6188509200150206 0.1271320507839345 6 6.716979637303383 0.026230622734929154 0.28112746782878123 1.0 True
25 7 10 7.620696678413973 134.60031955305035 0.5757088027733069 0.1313446077529101 6 5.755535843660075 0.012710332356906473 0.2297418063823675 1.0 True
26 8 9 7.803713858547152 12.027343338722998 0.6326834719980131 0.12084265385763356 6 5.539689880493177 0.02518064403521875 0.3859647522318924 1.0 True
27 8 10 8.062504137991457 43.97815524531457 0.6163861933423412 0.12936028463965984 6 4.789005902863088 0.011327540721525892 0.17059480981566058 1.0 True
28 8 11 11.084531710563947 22.606708130954026 0.5371195721380364 0.13463285672049757 6 5.833003178860405 0.017887959781482814 0.14919899299178907 1.0 True
29 9 10 2.04815062353057 56.00549858403758 0.6543345543345543 0.10729272360686735 6 3.3788563349731584 0.008725655639009402 0.02870462611907292 1.0 True
30 9 11 4.738677237611319 34.634051469677026 0.5818780055682106 0.11717687815100752 6 3.555986532172074 0.009813025835682346 0.0603546019895227 1.0 True
31 9 12 7.170741483679294 16.35622721712263 0.5379123584441162 0.12645695585785732 6 4.882558096197038 0.008862930161052695 0.11566409877698863 1.0 True
32 10 11 3.2047552083250137 21.371447114360556 0.7118898623279099 0.1192865608074921 6 3.2202034715576238 0.0029529340190147615 0.0041769080934441144 1.0 True
33 10 12 6.291813977735496 39.64927136691496 0.6120311738918656 0.1252565212812615 6 4.691686416856199 0.005686910809265337 0.10204044727301474 1.0 True
34 10 13 10.199392557022867 72.41150002956134 0.516551290119572 0.1354903305714049 6 7.346543684414892 0.01356504272158301 0.3255230434099281 1.0 True
35 11 12 3.467398797536633 18.277824252554396 0.650555275113579 0.12104882943540958 6 4.612247786063486 0.003583199374499245 0.03300207707174736 1.0 True
36 11 13 7.516502113110916 51.04005291520078 0.5698054068172914 0.12858662743221627 6 7.539783468898054 0.016001435752891854 0.11059625579949509 1.0 True
37 11 14 3.767517331528496 20.548768889074672 0.6420881321982974 0.12414062948335584 6 4.9522650472668115 0.012369101516230236 0.018705060406060074 1.0 True
38 12 13 4.049286119591895 32.762228662646386 0.6972966112450819 0.11680896213116294 6 4.282247993741361 0.011410238767832601 0.04779713993430858 1.0 True
39 12 14 0.97948616772873 2.2709446365202806 0.8749086479902558 0.09521299965540617 6 3.309695139564419 0.006159472215773642 0.014929948455572307 1.0 True
40 12 15 4.286747470271891 25.863710300929224 0.7022030893897189 0.11797995277580095 6 4.2772327867721325 0.008495008365045943 0.102782991447036 1.0 True
41 13 14 4.006260191078547 30.491284026126113 0.6955810147299509 0.1145595612270532 6 3.350289886810732 0.010228664633443074 0.03515966054944097 1.0 True
42 13 15 0.9562774815922267 6.898518361717157 0.868300353819945 0.10454213568084784 6 3.243735713395398 0.0023750253827712867 0.010725047644197173 1.0 True
43 13 16 3.565173336606111 18.944899794614482 0.7265456392027422 0.10962529664062398 6 3.522751424445623 0.008958927594995584 0.0304143851242741 1.0 True
44 14 15 4.019575892829469 23.592765664408944 0.7120070334086913 0.11868441290330693 6 4.620592469502459 0.002571958018982041 0.05506919751152759 1.0 True
45 14 16 7.5676649485439835 49.43618382074059 0.5918615984405458 0.12229328437386527 6 7.149509813179243 0.014273957859022303 0.25325650727956367 1.0 True
46 14 17 5.910977627463022 0.8461207481731591 0.6694009445687298 0.12443900216431929 6 5.157741429696001 0.017895201000461415 0.10920228290609475 1.0 True
47 15 16 3.7301261399251735 25.84341815633164 0.702887537993921 0.11495230769293868 6 3.540289976352534 0.013545291843393993 0.033466251783377816 1.0 True
48 15 17 2.2049738368271745 24.438886412582093 0.7429531936901991 0.11679524427533879 6 3.526664394280145 0.00989411002791081 0.07786907370564648 1.0 True
49 15 18 4.7000039832559155 3.452521908779401 0.7209645010046886 0.11716134583909153 6 4.125231895423432 0.011654729311847106 0.13683586564190353 1.0 True
50 16 17 3.368526196086246 50.282304568913744 0.618922305764411 0.11254196340939995 6 4.068632188828396 0.03104786021350874 0.10375098145235381 1.0 True
51 16 18 3.5240348999326185 22.39089624755224 0.6890156918687589 0.11084024896736888 6 4.42116710884217 0.01556225520371614 0.02495881796886513 1.0 True
52 16 19 2.146829294717985 30.035090485266103 0.8685060899826 0.10135543575024519 6 2.92007223300188 0.002952251383831446 0.02753369989560042 1.0 True
53 17 18 2.640403049812329 27.891408321361506 0.7697708305735859 0.10648049893472207 6 3.7928223564531667 0.010582276181446382 0.039599051949106026 1.0 True
54 17 19 3.933985934417215 80.31739505417984 0.6293759512937596 0.1095449771750205 6 3.6483069293931876 0.012240937390583118 0.060306184678878015 1.0 True
55 17 20 4.2254212089887 152.98392843416656 0.6014520938674964 0.12300562605352797 6 4.719447385686107 1.3272837904090529 0.4474442011368189 1.0 False forward_reverse_translation
56 18 19 2.4460967931915643 52.425986732818345 0.6827314510833881 0.10834806615100012 6 3.5073857685652805 0.012418496053917759 0.11905018881098527 1.0 True
57 18 20 6.593931011285688 125.09252011280485 0.5756313809779688 0.1265759478434112 6 5.389095141151797 0.012917057356045326 0.24042373011343365 1.0 True
58 18 21 11.793089868757727 175.70238585456048 0.4044519656339495 0.14396876383759685 6 10.041635341774429 0.06610149517267662 0.6118050873537593 1.0 False forward_reverse_rotation
59 19 20 6.650720121741557 72.6665333799865 0.6234734541714874 0.12775305804057488 6 5.958603794025073 0.009237066767190358 0.22540488888099894 1.0 True
60 19 21 12.053580264031138 123.27639912174077 0.3788200074840963 0.14687803396129923 6 10.170069582593054 0.1645149893124099 1.1204113986936788 1.0 False forward_reverse_translation;forward_reverse_rotation
61 19 22 14.836246959975925 128.85294276465592 0.3215252152521525 0.1505833656059155 6 15.25410245430046 0.08605091089946505 0.48945936193054473 1.0 False heldout_inlier_ratio;forward_reverse_translation
62 20 21 5.40286046809103 50.60986574175429 0.6596992097884272 0.12147955429086157 6 4.0820327359553845 0.014725537493637062 0.1988422472286775 1.0 True
63 20 22 8.200956565000565 56.18640938466938 0.5739414499308958 0.13255415786946786 6 5.328486341352201 0.006040617548520927 0.19706632354687328 1.0 True
64 20 23 5.175118275082073 70.79178325235415 0.6456945156330087 0.12075756038041646 6 3.9642702950866386 0.022949011013485506 0.1911210947924365 1.0 True
65 21 22 2.8644847627909416 5.57654364291509 0.7716237647919971 0.11632271358541554 6 2.79620153032919 0.00996357879827298 0.15332892195322273 1.0 True
66 21 23 1.2936223973175418 20.18191751059984 0.8576224819696593 0.09865676260631216 6 3.0367950693845156 0.0039225733250952055 0.00898487649368532 1.0 True
67 21 24 2.3128500583741403 54.59467208208593 0.7715940569126165 0.1136150455355286 6 2.76815824933335 0.007475673605589086 0.042277690012993266 1.0 True
68 22 23 3.7194009250537223 14.605373867684753 0.7396689147762109 0.11702962848624102 6 3.4148047250889095 0.01814015943430581 0.11847321816106485 1.0 True
69 22 24 4.786117710081478 49.01812843917083 0.6983240223463687 0.11898873157361621 6 3.6334655936865663 0.013438931050857202 0.08244161259412694 1.0 True
70 22 25 2.3747421598149763 12.391903814042255 0.736861094407697 0.11586394710110075 6 2.383007054117407 0.018559552524775386 0.0645260279790244 1.0 True
71 23 24 1.0880644725336985 34.41275457148609 0.7853164556962026 0.1128725442321241 6 2.409921828847164 0.002195759849115173 0.032112959181416705 1.0 True
72 23 25 5.033935954106079 2.213470053642494 0.6881127450980392 0.12303206700403986 6 2.9363727218037994 0.004939188019100004 0.12964064637100806 1.0 True
73 23 26 5.765534437288107 40.730927532824346 0.6852618757612667 0.12040544972155913 6 3.0590065093977192 0.006032445250250899 0.14039335223024835 1.0 True
74 24 25 6.097212810998921 36.62622462512857 0.677667493796526 0.12346315805814134 6 3.6286524357748307 0.02347922689116974 0.16273859629352566 1.0 True
75 24 26 6.852918693116795 6.318172961338249 0.6530209617755857 0.12710147612984257 6 3.5331859775372005 0.013311199903818141 0.18193579850152686 1.0 True
76 24 27 7.47982907414204 31.60254826458195 0.6649014778325123 0.1248115961442785 6 4.10006355979974 0.017872821205486625 0.1680556511699705 1.0 True
77 25 26 1.2882746074869595 42.944397586466835 0.9127837514934289 0.0958142916589384 6 2.9017658682436953 0.003534148840172682 0.018440169300173608 1.0 True
78 25 27 1.7666235781584831 68.22877288971053 0.8510739856801909 0.10418150333394147 6 2.368253417089997 0.006103232696002816 0.13366555345652814 1.0 True
79 25 28 2.1807182972588706 88.09106909546726 0.8853518429870751 0.10761251229652005 6 2.6372860976794645 0.003483231665912094 0.0369774420199415 1.0 True
80 26 27 0.6269948654714946 25.284375303243706 0.9183867141162515 0.09074909570650921 6 2.8142315359282506 0.00040199505815422204 0.011554314408129918 1.0 True
81 26 28 0.9431372257108486 45.14667150900044 0.8853200095170116 0.10604184939655073 6 2.656402027789519 0.007320240476189879 0.12930335868605478 1.0 True
82 26 29 1.129925637779203 87.41573662125148 0.7880466815984911 0.10871274240898265 6 3.111957466886604 0.0063952430610542755 0.039517035902874385 1.0 True
83 27 28 0.44809323479571145 19.862296205756735 0.9289448669201521 0.0850509551532673 6 2.96325286846982 0.005157655135591474 0.022666807877311387 1.0 True
84 27 29 1.160064136065596 62.13136131800778 0.7872365477452019 0.10455513464835667 6 2.9621627623005296 0.011650639607012138 0.1624719134077532 1.0 True
85 27 30 4.25412615059095 152.09392556214777 0.04792444029850746 0.15706827084964883 6 3.4115087753552786 2.5641513880069633 2.874944882076104 0.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
86 28 29 1.5995723610319212 42.26906511225104 0.8000944621560987 0.1086547547358129 6 3.1466224100058553 0.0011394164967304414 0.02119627748719656 1.0 True
87 28 30 4.295353381127667 132.23162935639104 0.042095416276894296 0.16025490727880456 6 4.631028587407634 2.5607552487652696 2.17230408479254 0.0 False heldout_inlier_ratio;heldout_inlier_rmse;forward_reverse_translation;forward_reverse_rotation;multistart_instability
88 28 31 5.754426930327516 158.8421298011293 0.7465330381074466 0.1066966653439246 6 2.402598366822474 0.00635764905956918 0.040016615214377715 1.0 True
89 29 30 4.099530130205247 89.96256424413995 0.7248812145092132 0.10870831469083347 6 2.9471223804737057 0.00465638537716095 0.12465763189227934 1.0 True
90 29 31 4.890018768530818 116.57306468887772 0.7243012243012243 0.11138751941762699 6 2.7645021322697017 0.004263906321896464 0.046637396086304225 1.0 True
91 29 32 4.464737187763289 159.39493219688632 0.6956070563818748 0.1103354081949904 6 3.227012588611917 0.011088061861347823 0.34561739459504637 1.0 True
92 30 31 2.3862917603439455 26.61050044473775 0.8185562292643862 0.0961579573295129 6 2.899558208444643 0.004999163455591531 0.0213847958734735 1.0 True
93 30 32 1.1507634071714652 69.43236795274659 0.8041343079031521 0.099533431536842 6 2.6146168742247573 0.003171376495143 0.02376737774843464 1.0 True
94 30 33 2.4361832484951105 160.7145717128102 0.0824534345711235 0.13003518012314966 6 7.2913036106936 1.3743823383381788 1.9837113244583153 0.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
95 31 32 1.2514382602180638 42.82186750800885 0.8157085941946499 0.09925944770258267 6 3.0485411400768245 0.004719596234887584 0.06146741991681224 1.0 True
96 31 33 0.641507519046697 134.10407126807203 0.0911563017261764 0.12702324561674966 6 7.541483837366941 2.816294381538612 3.333105317420589 0.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
97 32 33 1.4370687242949811 91.2822037600631 0.7569928006609229 0.10008740880870744 6 3.6611044282667207 2.988193350509204 2.696443494598379 1.0 False forward_reverse_translation;forward_reverse_rotation
results/02_data4_calibration/open3d_gicp/B_quality.json
-12004
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results/02_data4_calibration/open3d_gicp/B_refined.npz
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results/02_data4_calibration/open3d_gicp/B_refined.refinement.json
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results/02_data4_calibration/open3d_gicp/extrinsic.json
-414
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@@ -1,414 +0,0 @@
{
"schema_version": 2,
"success": true,
"message": "`ftol` termination condition is satisfied.",
"convention": "T_body_lidar maps raw LiDAR points into rear-axle body frame",
"equation": "A_ij X = X B_ij",
"measured_extrinsic_used_as_initial": false,
"translation_m": [
1.2975484155117711,
-0.0014693441955134902,
0.7052088406121522
],
"rotation_rpy_deg_xyz": [
-0.8301548470744382,
1.3787117674135543,
-0.9873301733452331
],
"quaternion_xyzw": [
-0.007139952974435751,
0.012092890421885221,
-0.008527968948116832,
0.9998650192992987
],
"matrix_4x4": [
[
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],
[
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results/02_data4_calibration/small_gicp/B_estimation.npz
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results/02_data4_calibration/small_gicp/B_quality.csv
-156
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@@ -1,156 +0,0 @@
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9,14,7.8912187855633125,14.085282580602351,0.5416463116756228,0.12859551377809317,6,26.721469573230642,0.011377143482081005,0.045883349808202654,1.0,True,
10,11,3.2047552083250137,21.371447114360556,0.7131414267834794,0.12095106901516857,6,13.404202373771971,0.006897671932212844,0.18540056788520093,1.0,True,
10,12,6.291813977735496,39.64927136691496,0.6117876278616659,0.125002241212049,6,19.806559061723195,0.006509808900811586,0.06742381610992963,1.0,True,
10,13,10.199392557022867,72.41150002956134,0.5244808055380743,0.13689263771908436,6,36.18235272080672,0.0046518429660042555,0.17931102925270673,1.0,True,
10,14,6.819808471778838,41.920216003435236,0.5996858385693572,0.12955691635611957,6,17.916357473627098,0.009593578345611205,0.05753783232412823,1.0,True,
10,15,10.561163834622734,65.51298166784417,0.5048970366649924,0.13966622273060364,6,30.173625507677322,0.006074999657630774,0.04867559411570644,1.0,True,
11,12,3.467398797536633,18.277824252554396,0.6508076728924785,0.12017753597340274,6,15.211760062254479,0.016302173123952383,0.05141023051579345,1.0,True,
11,13,7.516502113110916,51.04005291520078,0.5666710199817161,0.12966061077144844,6,26.14207974844453,0.010250004424021303,0.0630753341579125,1.0,True,
11,14,3.767517331528496,20.548768889074672,0.64271407110666,0.12109534664165014,6,14.04589685067402,0.007610676428698485,0.05350102444543152,1.0,True,
11,15,7.712397127778454,44.141534553483616,0.570479416362689,0.12836105648377344,6,18.857231656557765,0.006898635416764969,0.028532809525243653,1.0,True,
11,16,11.0395297023812,69.98495270981527,0.4761423882857864,0.13515636052103228,6,45.4042112112498,0.037021121414326036,0.5129154434742894,1.0,False,forward_reverse_rotation
12,13,4.049286119591895,32.762228662646386,0.7056733087955325,0.11689859246507509,6,14.509757355016381,0.0020816591744367207,0.08889045467662131,1.0,True,
12,14,0.97948616772873,2.2709446365202806,0.8745432399512789,0.09766069029332589,6,11.857488007889668,0.002473515580842477,0.018069288085025077,1.0,True,
12,15,4.286747470271891,25.863710300929224,0.7033426183844012,0.12103901189560114,6,11.859397045728326,0.022780233796594867,0.04927694758545139,1.0,True,
12,16,7.5836501880550475,51.70712845726087,0.5820235756385069,0.12458133956199553,6,32.351864267271,0.00989839849833309,0.03808519306435704,1.0,True,
12,17,6.351478009829798,1.4248238883471207,0.6542219994988725,0.12658997093251892,6,11.229951110937455,0.019742680150836883,0.07899236042083516,1.0,True,
13,14,4.006260191078547,30.491284026126113,0.6984766461034874,0.11406275231178287,6,13.924718120717541,0.012533581961738958,0.10861604448395465,1.0,True,
13,15,0.9562774815922267,6.898518361717157,0.8794391298650243,0.09903209123829654,6,10.514819201987352,0.006638809913641406,0.04266354358349458,1.0,True,
13,16,3.565173336606111,18.944899794614482,0.7273073505141552,0.10958532316684438,6,19.40505650408657,0.005441055528592516,0.12519365495730794,1.0,True,
13,17,2.967150651379281,31.337404774299262,0.7130265716137395,0.11779895987026284,6,12.816390530620689,0.013263327701592687,0.16221305155705806,1.0,True,
13,18,5.249882044431148,3.4459964529377567,0.7019876443728176,0.11940768524727291,6,25.985486690964983,0.008995185113784413,0.05198334813202341,1.0,True,
14,15,4.019575892829469,23.592765664408944,0.7035920622959055,0.12189498753760801,6,10.295943232842452,0.010817230757509965,0.008099442158890762,0.5,True,
14,16,7.5676649485439835,49.43618382074059,0.5923489278752436,0.12241126089476961,6,33.65365690477826,0.003104373432332903,0.13386003286196627,1.0,True,
14,17,5.910977627463022,0.8461207481731591,0.6687795177728063,0.12452036369759786,6,8.95305147071513,0.01159286549234958,0.10132041390926738,1.0,True,
14,18,8.548241724186095,27.045287573188347,0.6196476790536196,0.12845393182350578,6,17.805505225184664,0.010070495463159433,0.13041926369959572,1.0,True,
14,19,9.249254057266956,79.47127430600668,0.5845660749506904,0.12584132662356784,6,30.335272352492872,0.016830803029542436,0.2574761671757917,1.0,True,
15,16,3.7301261399251735,25.84341815633164,0.7032674772036475,0.11602216892855616,6,22.696890954829907,0.0009988867448423883,0.09036168134112703,1.0,True,
15,17,2.2049738368271745,24.438886412582093,0.7489009568140678,0.11923375870790391,6,6.944925131742929,0.02582458487951314,0.095039680889365,1.0,True,
15,18,4.7000039832559155,3.452521908779401,0.7165438713998661,0.11783922962071142,6,19.334165264487982,0.009354386824208725,0.17478401338848865,1.0,True,
15,19,5.238340351181781,55.87850864159773,0.6924358974358974,0.11506895717743916,6,20.41712608461488,0.012497900854287812,0.3255615001296644,1.0,True,
15,20,2.0380809160783224,128.5450420215843,0.6751867872591427,0.11901811643083532,6,27.381712286843808,0.007126007329084919,0.09473387178666172,1.0,True,
16,17,3.368526196086246,50.282304568913744,0.6284461152882206,0.1113618900510703,6,25.184005110635376,0.015370940622322818,0.07520125332617648,1.0,True,
16,18,3.5240348999326185,22.39089624755224,0.6921281286473868,0.10919235768364483,6,39.42922894015919,0.005355462743716769,0.03654303027439279,1.0,True,
16,19,2.146829294717985,30.035090485266103,0.8880188913745961,0.0968346861370537,6,11.70909430755386,0.006745004702225108,0.04851926363283949,1.0,True,
16,20,4.728364693236263,102.70162386525263,0.6307301587301587,0.12362874516637085,6,35.70048091760535,0.007128919591300971,0.10475436005961275,1.0,True,
16,21,10.094336026449797,153.311489607007,0.40465918895599656,0.14174574821871383,6,57.2667389243621,0.01442382622115287,0.2621394134233604,1.0,True,
17,18,2.640403049812329,27.891408321361506,0.7665916015366274,0.11462276561713726,6,9.759321661407776,0.003676908482849856,0.03455537183665902,1.0,True,
17,19,3.933985934417215,80.31739505417984,0.645738203957382,0.11467441399973681,6,29.93502749301381,0.002658154417044598,0.03997293849541112,1.0,True,
17,20,4.2254212089887,152.98392843416656,0.6031375599636977,0.12542519992547976,6,27.708613247812377,4.543486491493239,1.5708713272202308,0.5,False,forward_reverse_translation;forward_reverse_rotation
17,21,9.21529677253323,156.40620582407942,0.4672368255565338,0.13751533766984328,6,32.19730123852794,0.13403735422224766,1.1805230479043929,0.5,False,forward_reverse_translation;forward_reverse_rotation
17,22,11.781237287606983,150.82966218116434,0.4255952380952381,0.1417851723285674,6,38.71155142811069,0.01638337185583796,0.14112774930143776,1.0,True,
18,19,2.4460967931915643,52.425986732818345,0.6999343401181878,0.10876407223187468,6,36.919102980185954,0.0029722527816906422,0.028553700929610463,1.0,True,
18,20,6.593931011285688,125.09252011280485,0.5796614723267061,0.12778176089815,6,32.46060078153023,0.0849644734357879,0.24110896992060843,1.0,False,forward_reverse_translation
18,21,11.793089868757727,175.70238585456048,0.42345743296016664,0.14463219993853524,6,41.677643204951686,0.008594432490503082,0.044791316017877565,1.0,True,
18,22,14.40479158879449,178.72107050256645,0.35586914688903143,0.14801012931814386,6,118.81863294454718,0.05364920712348454,0.15560820359743163,1.0,True,
18,23,11.744734461596845,164.11569663484164,0.3967277486910995,0.14073241205403209,6,67.8157115569044,0.0379980141073483,0.09667096210106078,1.0,True,
19,20,6.650720121741557,72.6665333799865,0.6260444787247719,0.12668491637849374,6,25.97982149253148,0.022781715838261912,0.19834674514582162,1.0,True,
19,21,12.053580264031138,123.27639912174077,0.3952850193339154,0.1421446220617431,6,61.553005632745744,0.01796074748252817,0.16263425635632095,1.0,True,
19,22,14.836246959975925,128.85294276465592,0.3237392373923739,0.15069330869045588,6,75.70505048512227,0.2579934626807855,1.1847082611411373,1.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
19,23,11.740810561661805,143.45831663234065,0.37768025078369905,0.1432975121556299,6,38.93505210462392,0.07411982241929344,0.4458370864472602,1.0,True,
19,24,11.093516369446519,177.87107120383473,0.43504761904761907,0.13924446561169374,6,41.93660606132572,0.04258350776081339,0.7879276702809055,0.5,False,forward_reverse_rotation
20,21,5.40286046809103,50.60986574175429,0.6607188376242672,0.12214671257866079,6,14.082798146569438,0.01471691635641488,0.0747935794181239,1.0,True,
20,22,8.200956565000565,56.18640938466938,0.576328684508104,0.13245799460807672,6,16.034011252149995,0.02165175771302933,0.4612076789897289,1.0,True,
20,23,5.175118275082073,70.79178325235415,0.6451819579702717,0.12102263178677423,6,12.511468996120387,0.018249896901851893,0.12115759970382113,1.0,True,
20,24,4.710308396362637,105.20453782384023,0.7383177570093458,0.11441422046802802,6,12.152737785424229,0.009011279667019194,0.06430292411421322,1.0,True,
20,25,8.226491803107123,68.57831319871164,0.6182822702159718,0.12791257682460647,6,29.104161441720954,0.014703530260578728,0.32055535743282454,1.0,True,
21,22,2.8644847627909416,5.57654364291509,0.7740636818348177,0.11564788773425824,6,12.209574213097333,0.0065184199045192235,0.06991965908247275,1.0,True,
21,23,1.2936223973175418,20.18191751059984,0.862223327530465,0.10307116830776755,6,11.124499228103288,0.0026785916987678697,0.01879221122979615,1.0,True,
21,24,2.3128500583741403,54.59467208208593,0.7795265676152102,0.11182158240581822,6,15.934013426145448,0.003491995501356654,0.0374256510953578,1.0,True,
21,25,3.793272903723766,17.968447456957346,0.7340892465252378,0.12002239056891174,6,16.57216298005221,0.037735058311297705,0.2878144229712423,1.0,True,
21,26,4.631707245574304,60.912845043424184,0.7147358216190014,0.11997311573294966,6,17.372315653280065,0.012351932089198563,0.10379217190868555,1.0,True,
22,23,3.7194009250537223,14.605373867684753,0.7408951563458002,0.11726725109752717,6,12.610213323576254,0.009206244303295173,0.09601986001481369,1.0,True,
22,24,4.786117710081478,49.01812843917083,0.6936064556176288,0.11914624513148218,6,13.20338761495324,0.006090737153726605,0.027557138417491776,1.0,True,
22,25,2.3747421598149763,12.391903814042255,0.7356584485868911,0.11474070552638106,6,16.673633938013044,0.0019617091597581428,0.011643770804738128,1.0,True,
22,26,2.381693697571969,55.336301400509086,0.7422594142259414,0.11765221626900083,6,18.880420396501957,0.007814937371704293,0.039342553564881436,1.0,True,
22,27,2.9603952927690247,80.62067670375279,0.7254925373134329,0.11870181965154768,6,21.309389349405485,0.018241823604788005,0.08279236858582419,1.0,True,
23,24,1.0880644725336985,34.41275457148609,0.7884810126582279,0.10662565692629543,6,10.611199681165669,0.001882338148224756,0.020239211377623818,1.0,True,
23,25,5.033935954106079,2.213470053642494,0.6843137254901961,0.12171091934898426,6,15.150809064137142,0.014639828785635214,0.2046006298281892,1.0,True,
23,26,5.765534437288107,40.730927532824346,0.6772228989037758,0.12237954766026346,6,16.382334072569904,0.04518647006837157,0.20367965681897066,1.0,True,
23,27,6.392376191887118,66.01530283606805,0.6637469586374696,0.12340951265232122,6,20.64383986204732,0.010626193556947957,1.1817079481882757,1.0,False,forward_reverse_rotation
23,28,6.631292861991925,85.8775990418248,0.6720351390922401,0.12122778564083748,6,19.20657067904157,0.043540468687755365,0.26051478047218213,1.0,True,
24,25,6.097212810998921,36.62622462512857,0.6764267990074442,0.12367636388755719,6,21.50176872604179,0.035816405766439664,0.2046504337319023,1.0,True,
24,26,6.852918693116795,6.318172961338249,0.6524044389642417,0.12740232296321938,6,21.753053297058692,0.062038641373543625,0.12012273822228971,1.0,True,
24,27,7.47982907414204,31.60254826458195,0.6546798029556651,0.12425657449629145,6,26.295019203914197,0.05403266260962086,0.2126374478532415,1.0,True,
24,28,7.71913193400284,51.4648444703387,0.6614377470355731,0.12010155595807545,6,20.98500095492876,0.049490676797893776,0.25138526875505346,1.0,True,
24,29,7.213852273638132,93.73390958258973,0.6620579958399608,0.12331341986261203,6,28.045882177977884,0.08010108996241684,0.7934458070763889,1.0,False,forward_reverse_translation;forward_reverse_rotation
25,26,1.2882746074869595,42.944397586466835,0.9137395459976105,0.08148336624555251,6,16.56293455758718,0.0032405549728505064,0.006669174585592186,1.0,True,
25,27,1.7666235781584831,68.22877288971053,0.8596658711217183,0.1096145882030974,6,22.188153380460985,0.010624789877375215,0.04475651255674721,1.0,True,
25,28,2.1807182972588706,88.09106909546726,0.8839157491622786,0.10490699814192792,6,16.17217948348072,0.00283419137499405,0.01585308444597649,1.0,True,
25,29,1.118287177357716,130.36013420771832,0.7857227558401518,0.10954358768244267,6,20.132087187715307,0.003247718742817276,0.01654235380830592,1.0,True,
25,30,5.200639419572971,139.6773015481418,0.05061061531235322,0.1582745227634446,6,186.52424080569466,2.4401001990983833,2.63047633237589,0.0,False,heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation;multistart_instability
26,27,0.6269948654714946,25.284375303243706,0.9188612099644128,0.07520973241900301,6,20.799051531446562,0.0009920873307779914,0.005485007138529723,1.0,True,
26,28,0.9431372257108486,45.14667150900044,0.9182726623840114,0.07611768518866212,6,21.99541958409829,0.004984978187527545,0.01173507098896779,1.0,True,
26,29,1.129925637779203,87.41573662125148,0.7856890251090416,0.10851393061782179,6,24.280602674778656,0.002586732426991948,0.12205883610743135,1.0,True,
26,30,4.786676837961511,177.3783008654109,0.7634835395750642,0.10495724850674527,6,34.80647378548578,0.008489213184554775,0.021733210949123293,1.0,True,
26,31,5.900105669197046,156.01119868987135,0.7374054682955207,0.1073752166304431,6,38.310555401782544,3.545322953478412,6.459518815762061,1.0,False,forward_reverse_translation;forward_reverse_rotation
27,28,0.44809323479571145,19.862296205756735,0.9281131178707225,0.07220943509634768,6,20.093208902964978,0.0023003913248606234,0.001591751329336308,1.0,True,
27,29,1.160064136065596,62.13136131800778,0.7871188037207112,0.10966659884725236,6,24.009650087098436,0.005134403698947268,0.02434844099203523,1.0,True,
27,30,4.25412615059095,152.09392556214777,0.7922108208955224,0.10147035321534553,6,32.52026042532504,2.795897032209197,3.237329131939042,1.0,False,forward_reverse_translation;forward_reverse_rotation
27,31,5.542871649427442,178.70442600700352,0.7592097617664149,0.10539218018667602,6,48.70669958381899,0.0020939062619997405,0.027400662783563425,1.0,True,
27,32,4.8829350360277,138.47370648510565,0.7480278422273782,0.10408734715846867,6,42.21510199826003,0.004407463145306061,0.03490543724836902,1.0,True,
28,29,1.5995723610319212,42.26906511225104,0.787814381863266,0.10901449493832843,6,25.913625419675043,0.01570175790237609,0.03569660498137675,1.0,True,
28,30,4.295353381127667,132.23162935639104,0.7791159962581852,0.10438560755009844,6,36.501677925790155,0.01170149615735483,0.047047156693309715,1.0,True,
28,31,5.754426930327516,158.8421298011293,0.7449015266285981,0.10668967013687296,6,48.04636971250818,0.024421374377245158,0.6810283060803514,0.5,False,forward_reverse_rotation
28,32,5.013331477001236,158.33600269086247,0.7309213587715216,0.1081318944415262,6,52.23779663520043,0.10324080390042788,0.9479198721286788,1.0,False,forward_reverse_translation;forward_reverse_rotation
28,33,5.317102997899902,67.05379893079937,0.6692465836255895,0.10871806386783629,6,54.67376255043738,0.13259351588266985,0.659391929056215,1.0,False,forward_reverse_translation;forward_reverse_rotation
29,30,4.099530130205247,89.96256424413995,0.7320662880982732,0.10929719051930432,6,30.745355096911158,0.005463604154015333,0.018583483487870766,1.0,True,
29,31,4.890018768530818,116.57306468887772,0.7254562254562255,0.11323336570178015,6,49.09678922599757,0.0050777995851252426,0.07265894091769333,1.0,True,
29,32,4.464737187763289,159.39493219688632,0.695837657096737,0.10999820041072961,6,72.25486343140591,0.011220553177198233,0.14599088546526143,1.0,True,
29,33,4.34670323578768,109.32286404305042,0.6382698298586149,0.11215583949685817,6,72.66655529463955,0.018977478007060265,0.05615608666616428,1.0,True,
30,31,2.3862917603439455,26.61050044473775,0.8286237272623269,0.0964659694513182,6,41.03513305025223,0.018905314487385798,0.08632503464137033,1.0,True,
30,32,1.1507634071714652,69.43236795274659,0.8065326633165829,0.09815063400019142,6,35.25635856167173,0.007341509941520892,0.023973741904832375,1.0,True,
30,33,2.4361832484951105,160.7145717128102,0.6965239055641239,0.10032655671840386,6,47.08947384000088,2.6257249086936962,3.1197630566421646,1.0,False,forward_reverse_translation;forward_reverse_rotation
31,32,1.2514382602180638,42.82186750800885,0.8146841206602162,0.09914628416022417,6,34.33173181492779,0.06936877058296162,0.45703051311645637,1.0,True,
31,33,0.641507519046697,134.10407126807203,0.7551430598250177,0.1003246191461096,6,28.537349904719164,0.005559091155811048,0.0330053182501105,1.0,True,
32,33,1.4370687242949811,91.2822037600631,0.043432078366576185,0.1661924920133195,6,88.4956235178996,0.946171384319174,3.5092968853566924,0.0,False,heldout_inlier_ratio;heldout_inlier_rmse;forward_reverse_translation;forward_reverse_rotation;multistart_instability
1 i j rtk_translation_m rtk_rotation_deg heldout_inlier_ratio heldout_inlier_rmse_m hessian_rank hessian_condition reverse_translation_m reverse_rotation_deg multistart_success_rate accepted rejection_reasons
2 0 1 1.8477087194158957 24.171297449440786 0.8193962748876044 0.11049677170760565 6 14.01384280930539 0.026680306344951596 0.12400141971813113 1.0 True
3 0 2 2.633731568575307 80.09074797031298 0.7525388867463684 0.11492533799497681 6 14.962108606915407 0.0018465318763874656 0.03093450642761156 1.0 True
4 0 3 6.923255970100826 79.91583883299243 0.628093901505486 0.12365050970311489 6 23.49058941554809 0.010524333328229862 0.23898233567763014 1.0 True
5 0 4 2.952974950373882 130.25781950514033 0.693351593625498 0.11485738628517483 6 16.30512452170669 2.3214755241040135 1.4768350244766943 1.0 False forward_reverse_translation;forward_reverse_rotation
6 0 5 8.205074921973447 88.82606603101337 0.6015065913370998 0.12959862169013578 6 26.276485388438196 0.013711901937629547 0.26024107991311357 1.0 True
7 1 2 1.7155362084417105 55.91945052087219 0.7981310803891449 0.11167434332282761 6 13.484382276710292 0.050742368484802715 0.5170998105709517 1.0 False forward_reverse_rotation
8 1 3 5.885389341942907 55.74454138355163 0.678820988438572 0.12109867185657652 6 27.478714016209608 0.008324315958289934 0.25689852176843175 1.0 True
9 1 4 2.3031736807956613 106.08652205569953 0.6772473651580905 0.11157492180388107 6 13.25032679930311 0.00802452890723869 0.011334867255723058 1.0 True
10 1 5 7.590375029412286 64.65476858157257 0.618779694923731 0.12786963556791484 6 21.376356908960595 0.012044684321695526 0.5185219918090508 1.0 False forward_reverse_rotation
11 1 6 8.114848491620153 8.108983632525 0.6933789087226231 0.12162612003144421 6 23.560279629760274 0.008258211706006158 0.08942722541817279 1.0 True
12 2 3 4.339151683168145 0.17490913732054883 0.7609663064208518 0.11583878636897658 6 13.299472485774093 0.00784220284106163 0.01711532312869618 1.0 True
13 2 4 0.6150408096900305 50.167071534827386 0.796748976299789 0.11484342359047915 6 12.041666425070249 0.011210942709507262 0.11462542679280045 1.0 True
14 2 5 5.894923525501676 8.735318060700383 0.7112112112112112 0.12001317983504071 6 15.619771158055366 0.011298647069299764 0.02158504824207679 1.0 True
15 2 6 6.5470565092896145 47.810466888347186 0.7254529329785886 0.11794151013960824 6 22.73936753420594 0.004729020804343234 0.026463705119922912 1.0 True
16 2 7 5.848768648120739 73.12203319515616 0.7569187603621987 0.11528530285715083 6 13.439936375510058 0.011994525026310043 0.05882394078371607 1.0 True
17 3 4 3.9735035126146885 50.34198067214791 0.6957378664695738 0.11619002437046372 6 17.769677378118345 0.006018969660459514 0.057234124307389854 1.0 True
18 3 5 2.3619393178889707 8.910227198020932 0.7989069680784996 0.1077590577814381 6 12.151555874812605 0.01353229173052898 0.06486032517688407 1.0 True
19 3 6 2.2300116834828536 47.63555775102664 0.8435613682092555 0.11222309863990207 6 13.914901775514604 0.009512765198853305 0.09743636874085052 1.0 True
20 3 7 2.450274232383144 72.94712405783562 0.8651898734177215 0.11184568072686094 6 12.975777248765134 0.010827690226297962 0.12520280777371934 1.0 True
21 3 8 1.9936391786054533 163.56928836557165 0.825590155700653 0.10787987267070262 6 12.959410142765178 0.0056088079192386986 0.021536601402145895 1.0 True
22 4 5 5.370771070097231 41.431753474126985 0.6652516676773802 0.12444356767629798 6 17.0278455355532 0.003155412913416372 0.05824374716659687 1.0 True
23 4 6 6.142548765456278 97.97753842317456 0.6551681807021851 0.12030857820705748 6 24.380739297719824 0.011212969141455763 0.08373658947114908 0.5 True
24 4 7 5.314106070657687 123.28910472998356 0.696936001976773 0.11799538994568655 6 16.89426071448657 0.00409529177569414 0.0519770010059835 0.5 True
25 4 8 5.8981084980454765 146.08873096228052 0.7129198332924737 0.11349036082807595 6 18.616925126379197 0.01249802665331653 0.04549823831834878 1.0 True
26 4 9 3.1650365894055956 158.11607430100375 0.6097234068478128 0.11677937926620108 6 25.94675052408701 0.00616984405743089 0.11548555225177864 0.5 True
27 5 6 1.9357867378988893 56.54578494904757 0.7604901596732269 0.11101741624951145 6 16.791344333152654 0.01028641250952863 0.031127073736479716 1.0 True
28 5 7 0.19501559913675365 81.85735125585654 0.8092687180764918 0.10777871969807898 6 15.20338641054919 0.010579714886860828 0.03333492483252218 1.0 True
29 5 8 1.8038807059856978 172.47951556359365 0.7295760721789643 0.11311887218463837 6 17.053462425193878 0.009759205787181369 0.05935114015788255 1.0 True
30 5 9 7.881613621164215 160.45217222486949 0.514987714987715 0.13057312144553648 6 36.76355827214787 0.013423709236357283 0.8456480282562385 1.0 False forward_reverse_rotation
31 5 10 7.671857805815354 143.54232919109316 0.5442391832766165 0.13173165083201857 6 26.337222871823027 0.00523999417676037 0.17134499262937464 1.0 True
32 6 7 2.130456053070114 25.311566306808988 0.8539354187689203 0.10462253085153163 6 12.502910766598342 0.002843314342407394 0.028424505305675103 1.0 True
33 6 8 0.24494500622098103 115.93373061454483 0.7757757757757757 0.1131640002846509 6 15.521747346102597 0.007224674181694773 0.10395594559619498 1.0 True
34 6 9 8.025985916230132 103.90638727582186 0.6009202835468226 0.1259448851291812 6 28.795189977892573 0.007975110549369148 0.07338758946554978 1.0 True
35 6 10 8.303463402700086 159.91188585985975 0.5363513347275187 0.13083118553166714 6 31.167661263990606 1.7146158775247784 14.688249303878628 1.0 False forward_reverse_translation;forward_reverse_rotation
36 6 11 11.328882063792355 138.54043874549896 0.4633337584491774 0.13769942152040132 6 58.377014583638996 0.023339879627157865 0.18888808553225306 1.0 True
37 7 8 1.9962664218365056 90.62216430773583 0.8340050377833753 0.11132245152738919 6 16.170350777028464 0.004806949653405585 0.02034039865601464 1.0 True
38 7 9 7.8811064994361235 78.59482096901284 0.6160971335586432 0.12637042711002067 6 24.17866477904224 0.012112046262530643 0.5927664140170301 1.0 False forward_reverse_rotation
39 7 10 7.620696678413973 134.60031955305035 0.5721183607775164 0.13040415535689726 6 20.571872802604858 0.010507785715791607 0.10324111221368969 1.0 True
40 7 11 10.356268683459426 113.22887243868989 0.5179098728976762 0.13593896500168 6 36.18276452817704 0.01570096854689765 0.062213006599939585 1.0 True
41 7 12 13.76202076313695 94.9510481861355 0.419173636250156 0.14753718360891266 6 89.28960611274712 0.018245717875293024 0.41212759156893974 1.0 True
42 8 9 7.803713858547152 12.027343338722998 0.6407549981373402 0.12189583104066963 6 28.06713348388497 0.025170709673794655 0.14112050756711447 1.0 True
43 8 10 8.062504137991457 43.97815524531457 0.6075420709986488 0.12933255373443525 6 20.429632894689156 0.01256038832173067 0.12386976869589704 1.0 True
44 8 11 11.084531710563947 22.606708130954026 0.5447599643448364 0.13547919137380177 6 27.72840331379869 0.006462771421800638 0.08629803406158434 1.0 True
45 8 12 14.350449135419003 4.328883878399632 0.46489164086687307 0.14530908872417966 6 37.958964738084276 0.00955628888567663 0.1112688142308669 1.0 True
46 8 13 18.245302761704593 28.43334478424675 0.37832991803278687 0.15442802982944018 6 101.4897081447731 0.027875630486669568 0.37387837894914167 1.0 True
47 9 10 2.04815062353057 56.00549858403758 0.6576312576312576 0.10693844153477818 6 15.957672354569297 0.0038184479242649575 0.016135929192430513 0.5 True
48 9 11 4.738677237611319 34.634051469677026 0.5883320678309288 0.11815838246331245 6 21.526672921842792 0.008866435423074387 0.05039949658393549 1.0 True
49 9 12 7.170741483679294 16.35622721712263 0.5413589364844904 0.12469816852157387 6 38.0354745717217 0.03403608365914784 0.249719392637206 1.0 True
50 9 13 10.733928030180744 16.40600144552375 0.5075728649611811 0.13356609480985904 6 50.3483756147633 0.0053243668825205025 0.1401976908512776 1.0 True
51 9 14 7.8912187855633125 14.085282580602351 0.5416463116756228 0.12859551377809317 6 26.721469573230642 0.011377143482081005 0.045883349808202654 1.0 True
52 10 11 3.2047552083250137 21.371447114360556 0.7131414267834794 0.12095106901516857 6 13.404202373771971 0.006897671932212844 0.18540056788520093 1.0 True
53 10 12 6.291813977735496 39.64927136691496 0.6117876278616659 0.125002241212049 6 19.806559061723195 0.006509808900811586 0.06742381610992963 1.0 True
54 10 13 10.199392557022867 72.41150002956134 0.5244808055380743 0.13689263771908436 6 36.18235272080672 0.0046518429660042555 0.17931102925270673 1.0 True
55 10 14 6.819808471778838 41.920216003435236 0.5996858385693572 0.12955691635611957 6 17.916357473627098 0.009593578345611205 0.05753783232412823 1.0 True
56 10 15 10.561163834622734 65.51298166784417 0.5048970366649924 0.13966622273060364 6 30.173625507677322 0.006074999657630774 0.04867559411570644 1.0 True
57 11 12 3.467398797536633 18.277824252554396 0.6508076728924785 0.12017753597340274 6 15.211760062254479 0.016302173123952383 0.05141023051579345 1.0 True
58 11 13 7.516502113110916 51.04005291520078 0.5666710199817161 0.12966061077144844 6 26.14207974844453 0.010250004424021303 0.0630753341579125 1.0 True
59 11 14 3.767517331528496 20.548768889074672 0.64271407110666 0.12109534664165014 6 14.04589685067402 0.007610676428698485 0.05350102444543152 1.0 True
60 11 15 7.712397127778454 44.141534553483616 0.570479416362689 0.12836105648377344 6 18.857231656557765 0.006898635416764969 0.028532809525243653 1.0 True
61 11 16 11.0395297023812 69.98495270981527 0.4761423882857864 0.13515636052103228 6 45.4042112112498 0.037021121414326036 0.5129154434742894 1.0 False forward_reverse_rotation
62 12 13 4.049286119591895 32.762228662646386 0.7056733087955325 0.11689859246507509 6 14.509757355016381 0.0020816591744367207 0.08889045467662131 1.0 True
63 12 14 0.97948616772873 2.2709446365202806 0.8745432399512789 0.09766069029332589 6 11.857488007889668 0.002473515580842477 0.018069288085025077 1.0 True
64 12 15 4.286747470271891 25.863710300929224 0.7033426183844012 0.12103901189560114 6 11.859397045728326 0.022780233796594867 0.04927694758545139 1.0 True
65 12 16 7.5836501880550475 51.70712845726087 0.5820235756385069 0.12458133956199553 6 32.351864267271 0.00989839849833309 0.03808519306435704 1.0 True
66 12 17 6.351478009829798 1.4248238883471207 0.6542219994988725 0.12658997093251892 6 11.229951110937455 0.019742680150836883 0.07899236042083516 1.0 True
67 13 14 4.006260191078547 30.491284026126113 0.6984766461034874 0.11406275231178287 6 13.924718120717541 0.012533581961738958 0.10861604448395465 1.0 True
68 13 15 0.9562774815922267 6.898518361717157 0.8794391298650243 0.09903209123829654 6 10.514819201987352 0.006638809913641406 0.04266354358349458 1.0 True
69 13 16 3.565173336606111 18.944899794614482 0.7273073505141552 0.10958532316684438 6 19.40505650408657 0.005441055528592516 0.12519365495730794 1.0 True
70 13 17 2.967150651379281 31.337404774299262 0.7130265716137395 0.11779895987026284 6 12.816390530620689 0.013263327701592687 0.16221305155705806 1.0 True
71 13 18 5.249882044431148 3.4459964529377567 0.7019876443728176 0.11940768524727291 6 25.985486690964983 0.008995185113784413 0.05198334813202341 1.0 True
72 14 15 4.019575892829469 23.592765664408944 0.7035920622959055 0.12189498753760801 6 10.295943232842452 0.010817230757509965 0.008099442158890762 0.5 True
73 14 16 7.5676649485439835 49.43618382074059 0.5923489278752436 0.12241126089476961 6 33.65365690477826 0.003104373432332903 0.13386003286196627 1.0 True
74 14 17 5.910977627463022 0.8461207481731591 0.6687795177728063 0.12452036369759786 6 8.95305147071513 0.01159286549234958 0.10132041390926738 1.0 True
75 14 18 8.548241724186095 27.045287573188347 0.6196476790536196 0.12845393182350578 6 17.805505225184664 0.010070495463159433 0.13041926369959572 1.0 True
76 14 19 9.249254057266956 79.47127430600668 0.5845660749506904 0.12584132662356784 6 30.335272352492872 0.016830803029542436 0.2574761671757917 1.0 True
77 15 16 3.7301261399251735 25.84341815633164 0.7032674772036475 0.11602216892855616 6 22.696890954829907 0.0009988867448423883 0.09036168134112703 1.0 True
78 15 17 2.2049738368271745 24.438886412582093 0.7489009568140678 0.11923375870790391 6 6.944925131742929 0.02582458487951314 0.095039680889365 1.0 True
79 15 18 4.7000039832559155 3.452521908779401 0.7165438713998661 0.11783922962071142 6 19.334165264487982 0.009354386824208725 0.17478401338848865 1.0 True
80 15 19 5.238340351181781 55.87850864159773 0.6924358974358974 0.11506895717743916 6 20.41712608461488 0.012497900854287812 0.3255615001296644 1.0 True
81 15 20 2.0380809160783224 128.5450420215843 0.6751867872591427 0.11901811643083532 6 27.381712286843808 0.007126007329084919 0.09473387178666172 1.0 True
82 16 17 3.368526196086246 50.282304568913744 0.6284461152882206 0.1113618900510703 6 25.184005110635376 0.015370940622322818 0.07520125332617648 1.0 True
83 16 18 3.5240348999326185 22.39089624755224 0.6921281286473868 0.10919235768364483 6 39.42922894015919 0.005355462743716769 0.03654303027439279 1.0 True
84 16 19 2.146829294717985 30.035090485266103 0.8880188913745961 0.0968346861370537 6 11.70909430755386 0.006745004702225108 0.04851926363283949 1.0 True
85 16 20 4.728364693236263 102.70162386525263 0.6307301587301587 0.12362874516637085 6 35.70048091760535 0.007128919591300971 0.10475436005961275 1.0 True
86 16 21 10.094336026449797 153.311489607007 0.40465918895599656 0.14174574821871383 6 57.2667389243621 0.01442382622115287 0.2621394134233604 1.0 True
87 17 18 2.640403049812329 27.891408321361506 0.7665916015366274 0.11462276561713726 6 9.759321661407776 0.003676908482849856 0.03455537183665902 1.0 True
88 17 19 3.933985934417215 80.31739505417984 0.645738203957382 0.11467441399973681 6 29.93502749301381 0.002658154417044598 0.03997293849541112 1.0 True
89 17 20 4.2254212089887 152.98392843416656 0.6031375599636977 0.12542519992547976 6 27.708613247812377 4.543486491493239 1.5708713272202308 0.5 False forward_reverse_translation;forward_reverse_rotation
90 17 21 9.21529677253323 156.40620582407942 0.4672368255565338 0.13751533766984328 6 32.19730123852794 0.13403735422224766 1.1805230479043929 0.5 False forward_reverse_translation;forward_reverse_rotation
91 17 22 11.781237287606983 150.82966218116434 0.4255952380952381 0.1417851723285674 6 38.71155142811069 0.01638337185583796 0.14112774930143776 1.0 True
92 18 19 2.4460967931915643 52.425986732818345 0.6999343401181878 0.10876407223187468 6 36.919102980185954 0.0029722527816906422 0.028553700929610463 1.0 True
93 18 20 6.593931011285688 125.09252011280485 0.5796614723267061 0.12778176089815 6 32.46060078153023 0.0849644734357879 0.24110896992060843 1.0 False forward_reverse_translation
94 18 21 11.793089868757727 175.70238585456048 0.42345743296016664 0.14463219993853524 6 41.677643204951686 0.008594432490503082 0.044791316017877565 1.0 True
95 18 22 14.40479158879449 178.72107050256645 0.35586914688903143 0.14801012931814386 6 118.81863294454718 0.05364920712348454 0.15560820359743163 1.0 True
96 18 23 11.744734461596845 164.11569663484164 0.3967277486910995 0.14073241205403209 6 67.8157115569044 0.0379980141073483 0.09667096210106078 1.0 True
97 19 20 6.650720121741557 72.6665333799865 0.6260444787247719 0.12668491637849374 6 25.97982149253148 0.022781715838261912 0.19834674514582162 1.0 True
98 19 21 12.053580264031138 123.27639912174077 0.3952850193339154 0.1421446220617431 6 61.553005632745744 0.01796074748252817 0.16263425635632095 1.0 True
99 19 22 14.836246959975925 128.85294276465592 0.3237392373923739 0.15069330869045588 6 75.70505048512227 0.2579934626807855 1.1847082611411373 1.0 False heldout_inlier_ratio;forward_reverse_translation;forward_reverse_rotation
100 19 23 11.740810561661805 143.45831663234065 0.37768025078369905 0.1432975121556299 6 38.93505210462392 0.07411982241929344 0.4458370864472602 1.0 True
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results/02_data4_calibration/small_gicp/B_quality.json
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results/02_data4_calibration/small_gicp/B_refined.npz
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results/02_data4_calibration/small_gicp/B_refined.refinement.json
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results/02_data4_calibration/small_gicp/extrinsic.json
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results/03_comparison/README.md
-7
View File
@@ -1,7 +0,0 @@
# 跨批比较
- `extrinsic_difference.json`：data4 X 相对历史部署 X 的严格 SE(3) 差，约 `1.592 cm / 0.234°`。
- `old_extrinsic_on_data4.json`：历史部署 X 在 data4 B 上的残差，约 `0.11953 m / 1.24836°`。
- `data4_extrinsic_on_previous_batch2.json`：data4 X 在历史第二批 B 上的残差，约 `0.07931 m / 0.98926°`。
两套 X 的跨批表现接近，而 data4 自身估计残差更高；因此维持历史部署值，data4 只作为候选。

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