/**:
  ros__parameters:
    # Topics
    pointCloudTopic: "/sensing/lidar/top/pointcloud"               # Point cloud data
    imuTopic: "/sensing/imu/tamagawa/imu_raw"                         # IMU data
    odomTopic: "odometry/imu"                   # IMU pre-preintegration odometry, same frequency as IMU
    gpsTopic: "/sensing/gnss/ublox/nav_sat_fix"                   # GPS odometry topic from navsat, see module_navsat.launch file

    # Frames
    lidarFrame: "base_link"
    baselinkFrame: "base_link"
    odometryFrame: "odom"
    mapFrame: "map"

    # GPS Settings
    useImuHeadingInitialization: true           # if using GPS data, set to "true"
    useGpsElevation: true                      # if GPS elevation is bad, set to "false"
    gpsCovThreshold: 2.0                        # m^2, threshold for using GPS data
    poseCovThreshold: 25.0                      # m^2, threshold for using GPS data
    
    # Export settings
    savePCD: false                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
    savePCDDirectory: "/Documents/map/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation

    # Sensor Settings
    sensor: robosense                            # lidar sensor type, 'velodyne' or 'ouster' or 'livox' or 'robosense'
    N_SCAN: 64                                  # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6)
    Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000)
    downsampleRate: 2                           # default: 1. Downsample your data if too many points(line). i.e., 16 = 64 / 4, 16 = 16 / 1
    point_filter_num: 1                         # default: 3. Downsample your data if too many points(point). e.g., 16: 1, 32: 5, 64: 8
    lidarMinRange: 1.0                          # default: 1.0, minimum lidar range to be used
    lidarMaxRange: 1000.0                       # default: 1000.0, maximum lidar range to be used

    # IMU Settings
    imuType: 0                                  # 0: 6-axis  1: 9-axis
    imuRate: 100.0
    imuAccNoise: 0.0001
    imuGyrNoise: 0.0001
    imuAccBiasN: 0.00001
    imuGyrBiasN: 0.00001
    imuGravity: 9.80511
    imuRPYWeight: 0.01

    # Extrinsics: T_lb (lidar -> imu)
    extrinsicTrans:  [0.019973, 0.01783, -0.895085]
    extrinsicRot: [1.0, 0.0, 0.0,
                    0.0, 1.0, 0.0,
                    0.0, 0.0, 1.0]

    # This parameter is set only when the 9-axis IMU is used, but it must be a high-precision IMU. e.g. MTI-680
    extrinsicRPY: [0.0, 0.0, 0.0,
                    0.0, 1.0, 0.0,
                    0.0, 0.0, 1.0]

    # voxel filter paprams
    mappingSurfLeafSize: 0.3                      # default: 0.4 - outdoor, 0.2 - indoor

    # robot motion constraint (in case you are using a 2D robot)
    z_tollerance: 1000.0                            # meters
    rotation_tollerance: 1000.0                     # radians

    # CPU Params
    numberOfCores: 4                              # number of cores for mapping optimization
    mappingProcessInterval: 0.0                  # seconds, regulate mapping frequency

    # Surrounding map
    surroundingkeyframeAddingDistThreshold: 1.0   # meters, regulate keyframe adding threshold
    surroundingkeyframeAddingAngleThreshold: 0.2  # radians, regulate keyframe adding threshold
    surroundingKeyframeDensity: 2.0               # meters, downsample surrounding keyframe poses   
    surroundingKeyframeSearchRadius: 50.0         # meters, within n meters scan-to-map optimization (when loop closure disabled)
    surroundingKeyframeMapLeafSize: 0.5           # downsample local map point cloud

    # Loop closure
    loopClosureEnableFlag: true
    loopClosureFrequency: 1.0                     # Hz, regulate loop closure constraint add frequency
    surroundingKeyframeSize: 50                   # submap size (when loop closure enabled)
    historyKeyframeSearchRadius: 15.0             # meters, key frame that is within n meters from current pose will be considerd for loop closure
    historyKeyframeSearchTimeDiff: 30.0           # seconds, key frame that is n seconds older will be considered for loop closure
    historyKeyframeSearchNum: 25                  # number of hostory key frames will be fused into a submap for loop closure
    loopClosureICPSurfLeafSize: 0.5               # downsample icp point cloud  
    historyKeyframeFitnessScore: 0.3              # icp threshold, the smaller the better alignment

    # Visualization
    globalMapVisualizationSearchRadius: 1000.0    # meters, global map visualization radius
    globalMapVisualizationPoseDensity: 1.0       # meters, global map visualization keyframe density
    globalMapVisualizationLeafSize: 0.5           # meters, global map visualization cloud density