86 lines
4.8 KiB
YAML
86 lines
4.8 KiB
YAML
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/**:
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ros__parameters:
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# Topics
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pointCloudTopic: "/sensing/lidar/top/pointcloud" # Point cloud data
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imuTopic: "/sensing/imu/tamagawa/imu_raw" # IMU data
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odomTopic: "odometry/imu" # IMU pre-preintegration odometry, same frequency as IMU
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gpsTopic: "/sensing/gnss/ublox/nav_sat_fix" # GPS odometry topic from navsat, see module_navsat.launch file
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# Frames
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lidarFrame: "base_link"
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baselinkFrame: "base_link"
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odometryFrame: "odom"
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mapFrame: "map"
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# GPS Settings
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useImuHeadingInitialization: true # if using GPS data, set to "true"
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useGpsElevation: true # if GPS elevation is bad, set to "false"
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gpsCovThreshold: 2.0 # m^2, threshold for using GPS data
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poseCovThreshold: 25.0 # m^2, threshold for using GPS data
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# Export settings
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savePCD: false # https://github.com/TixiaoShan/LIO-SAM/issues/3
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savePCDDirectory: "/Documents/map/" # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation
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# Sensor Settings
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sensor: robosense # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'robosense'
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N_SCAN: 64 # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6)
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Horizon_SCAN: 1800 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000)
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downsampleRate: 2 # default: 1. Downsample your data if too many points(line). i.e., 16 = 64 / 4, 16 = 16 / 1
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point_filter_num: 1 # default: 3. Downsample your data if too many points(point). e.g., 16: 1, 32: 5, 64: 8
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lidarMinRange: 1.0 # default: 1.0, minimum lidar range to be used
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lidarMaxRange: 1000.0 # default: 1000.0, maximum lidar range to be used
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# IMU Settings
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imuType: 0 # 0: 6-axis 1: 9-axis
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imuRate: 100.0
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imuAccNoise: 0.0001
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imuGyrNoise: 0.0001
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imuAccBiasN: 0.00001
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imuGyrBiasN: 0.00001
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imuGravity: 9.80511
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imuRPYWeight: 0.01
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# Extrinsics: T_lb (lidar -> imu)
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extrinsicTrans: [0.019973, 0.01783, -0.895085]
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extrinsicRot: [1.0, 0.0, 0.0,
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0.0, 1.0, 0.0,
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0.0, 0.0, 1.0]
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# This parameter is set only when the 9-axis IMU is used, but it must be a high-precision IMU. e.g. MTI-680
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extrinsicRPY: [0.0, 0.0, 0.0,
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0.0, 1.0, 0.0,
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0.0, 0.0, 1.0]
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# voxel filter paprams
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mappingSurfLeafSize: 0.3 # default: 0.4 - outdoor, 0.2 - indoor
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# robot motion constraint (in case you are using a 2D robot)
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z_tollerance: 1000.0 # meters
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rotation_tollerance: 1000.0 # radians
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# CPU Params
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numberOfCores: 4 # number of cores for mapping optimization
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mappingProcessInterval: 0.0 # seconds, regulate mapping frequency
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# Surrounding map
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surroundingkeyframeAddingDistThreshold: 1.0 # meters, regulate keyframe adding threshold
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surroundingkeyframeAddingAngleThreshold: 0.2 # radians, regulate keyframe adding threshold
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surroundingKeyframeDensity: 2.0 # meters, downsample surrounding keyframe poses
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surroundingKeyframeSearchRadius: 50.0 # meters, within n meters scan-to-map optimization (when loop closure disabled)
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surroundingKeyframeMapLeafSize: 0.5 # downsample local map point cloud
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# Loop closure
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loopClosureEnableFlag: true
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loopClosureFrequency: 1.0 # Hz, regulate loop closure constraint add frequency
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surroundingKeyframeSize: 50 # submap size (when loop closure enabled)
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historyKeyframeSearchRadius: 15.0 # meters, key frame that is within n meters from current pose will be considerd for loop closure
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historyKeyframeSearchTimeDiff: 30.0 # seconds, key frame that is n seconds older will be considered for loop closure
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historyKeyframeSearchNum: 25 # number of hostory key frames will be fused into a submap for loop closure
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loopClosureICPSurfLeafSize: 0.5 # downsample icp point cloud
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historyKeyframeFitnessScore: 0.3 # icp threshold, the smaller the better alignment
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# Visualization
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globalMapVisualizationSearchRadius: 1000.0 # meters, global map visualization radius
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globalMapVisualizationPoseDensity: 1.0 # meters, global map visualization keyframe density
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globalMapVisualizationLeafSize: 0.5 # meters, global map visualization cloud density
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