my_params_lidar.yaml

# project name
PROJECT_NAME: "lvi_sam"

lvi_sam:

  # Topics
  pointCloudTopic: "velodyne_points"               # Point cloud data
  imuTopic: "handsfree/imu"                         # IMU data

  # Heading
  useImuHeadingInitialization: false          # if using GPS data, set to "true"
  
  # Export settings
  savePCD: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
  savePCDDirectory: "/home/ljl/Documents/results/LIV_SAM_saveMap/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation

  # Sensor Settings
  N_SCAN: 32                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  timeField: "time"                           # point timestamp field, Velodyne - "time", Ouster - "t"
  downsampleRate: 1                           # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1 

  # IMU Settings
  imuAccNoise: 3.7686306102624571e-02
  imuGyrNoise: 2.3417543020438883e-03
  imuAccBiasN: 1.1416642385952368e-03
  imuGyrBiasN: 1.4428407712885209e-05
  imuGravity: 9.805
  
  # Extrinsics (lidar -> IMU)
  extrinsicTrans: [0.27255, -0.00053,0.17954]
  extrinsicRot: [1, 0, 0, 0, 1, 0, 0, 0, 1]
  extrinsicRPY: [1, 0, 0, 0, 1, 0, 0, 0, 1]

  # LOAM feature threshold
  edgeThreshold: 1.0
  surfThreshold: 0.1
  edgeFeatureMinValidNum: 10
  surfFeatureMinValidNum: 100

  # voxel filter paprams
  odometrySurfLeafSize: 0.4                     # default: 0.4
  mappingCornerLeafSize: 0.2                    # default: 0.2
  mappingSurfLeafSize: 0.4                      # default: 0.4

  # robot motion constraint (in case you are using a 2D robot)
  z_tollerance: 1000        # meters
  rotation_tollerance: 1000 # radians

  # CPU Params
  numberOfCores: 4                              # number of cores for mapping optimization
  mappingProcessInterval: 0.15                  # 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)

  # Loop closure
  loopClosureEnableFlag: true
  surroundingKeyframeSize: 25                   # 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
  historyKeyframeFitnessScore: 0.3              # icp threshold, the smaller the better alignment

  # Visualization
  globalMapVisualizationSearchRadius: 1000.0    # meters, global map visualization radius
  globalMapVisualizationPoseDensity: 10.0       # meters, global map visualization keyframe density
  globalMapVisualizationLeafSize: 1.0           # meters, global map visualization cloud density