lio-sam/config/params.yaml

lio_sam:

  useRing: false
  useOdom: true
  # Topics
  pointCloudTopic: "rslidar_points"               # Point cloud data
  imuTopic: "imu_data"                         # IMU data
  odomTopic: "odom"                   # IMU pre-preintegration odometry, same frequency as IMU
  gpsTopic: "odometry/gpsz"                   # 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: false                      # 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: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
  saveJson: true                              # https://github.com/TixiaoShan/LIO-SAM/issues/3
  savePCDDirectory: "/work/file/map/"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation
  mapPath: "/work/file/map/"

  # Sensor Settings
  N_SCAN: 16                                  # 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
  lidarMinRange: 1.0                         # default: 1.0, minimum lidar range to be used
  lidarMaxRange: 200.0                       # default: 1000.0, maximum lidar range to be used

  # IMU Settings
  imuAccNoise: 3.9939570888238808e-03
  imuGyrNoise: 1.5636343949698187e-03
  imuAccBiasN: 6.4356659353532566e-05
  imuGyrBiasN: 3.5640318696367613e-05
  imuGravity: 9.81
#  imuGravity: 0
  imuRPYWeight: 0.01
  odomYaw: 0
  extrinsicTrans: [ 0.0,0,0]
#  extrinsicTrans: [ -8.086759e-01, 3.195559e-01, -7.997231e-01 ]
#  extrinsicRPY: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]
#  extrinsicRot: [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]







  # Extrinsics (lidar -> IMU)
#  extrinsicTrans: [0.0, 0.0, 0.0]
#  extrinsicRot: [-1, 0, 0,
#                  0, 1, 0,
#                  0, 0, -1]
#  extrinsicRot: [ 0,  1, 0,
#                  -1, 0, 0,
#                  0, 0, 1 ]
#  extrinsicRPY: [0,  1, 0,
#                 -1, 0, 0,
#                  0, 0, 1]
  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: 0.2                     # default: 0.4 - outdoor, 0.2 - indoor
  mappingCornerLeafSize: 0.1                    # default: 0.2 - outdoor, 0.1 - indoor
  mappingSurfLeafSize: 0.2                      # default: 0.4 - outdoor, 0.2 - indoor

  # robot motion constraint (in case you are using a 2D robot)
  z_tollerance: 0                            # meters
  rotation_tollerance: 0                     # radians

  # CPU Params
  numberOfCores: 8                              # 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: 1.0               # meters, downsample surrounding keyframe poses
  surroundingKeyframeSearchRadius: 50.0         # meters, within n meters scan-to-map optimization (when loop closure disabled)

  # Loop closure
  loopClosureEnableFlag: true
  loopClosureFrequency: 2.0                     # Hz, regulate loop closure constraint add frequency
  surroundingKeyframeSize: 50                   # submap size (when loop closure enabled)
  historyKeyframeSearchRadius: 2.0             # 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: 1.0       # meters, global map visualization keyframe density
  globalMapVisualizationLeafSize: 0.2           # meters, global map visualization cloud density

  #Localization
  addKeyFameNum: 0
  ndtMinScore: 1.0



# Navsat (convert GPS coordinates to Cartesian)
navsat:
  frequency: 50
  wait_for_datum: false
  delay: 0.0
  magnetic_declination_radians: 0
  yaw_offset: 0
  zero_altitude: true
  broadcast_utm_transform: false
  broadcast_utm_transform_as_parent_frame: false
  publish_filtered_gps: false

# EKF for Navsat
ekf_gps:
  publish_tf: false
  map_frame: map
  odom_frame: odom
  base_link_frame: base_link
  world_frame: odom

  frequency: 50
  two_d_mode: false
  sensor_timeout: 0.01
  # -------------------------------------
  # External IMU:
  # -------------------------------------
  imu0: imu_correct
  # make sure the input is aligned with ROS REP105. "imu_correct" is manually transformed by myself. EKF can also transform the data using tf between your imu and base_link
  imu0_config: [ false, false, false,
                 true,  true,  true,
                 false, false, false,
                 false, false, true,
                 true,  true,  true ]
  imu0_differential: false
  imu0_queue_size: 50
  imu0_remove_gravitational_acceleration: true
  # -------------------------------------
  # Odometry (From Navsat):
  # -------------------------------------
  odom0: odometry/gps
  odom0_config: [ true,  true,  true,
                  false, false, false,
                  false, false, false,
                  false, false, false,
                  false, false, false ]
  odom0_differential: false
  odom0_queue_size: 10

  #                            x     y     z     r     p     y   x_dot  y_dot  z_dot  r_dot p_dot y_dot x_ddot y_ddot z_ddot
  process_noise_covariance: [ 1.0,  0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    1.0,  0,    0,    0,    0,    0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    10.0, 0,    0,    0,    0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0.03, 0,    0,    0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0.03, 0,    0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0.1,  0,     0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0.25,  0,     0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0.25,  0,     0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0.04,  0,    0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0.01, 0,    0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0.01, 0,    0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0.5,  0,    0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,    0.01, 0,      0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,    0,    0.01,   0,
                              0,    0,    0,    0,    0,    0,    0,     0,     0,     0,    0,    0,    0,    0,      0.015 ]