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LIO-SAM params.yaml
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lio_sam: | |
# Topics | |
pointCloudTopic: "points_raw" # Point cloud data | |
imuTopic: "imu_raw" #"imu/data" # # IMU data | |
odomTopic: "odometry/imu" # 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: false # 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: false # https://github.com/TixiaoShan/LIO-SAM/issues/3 | |
savePCDDirectory: "/Downloads/LOAM/" # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation | |
# Sensor Settings | |
sensor: livox # lidar sensor type, 'velodyne' or 'ouster' or 'livox' | |
N_SCAN: 6 # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6) | |
Horizon_SCAN: 4000 # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048, Livox Horizon: 4000) | |
downsampleRate: 1 # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1 | |
lidarMinRange: 3 # default: 1.0, minimum lidar range to be used | |
lidarMaxRange: 100.0 # 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: 9.80511 | |
imuRPYWeight: 0.01 | |
# Extrinsics: T_lb (lidar -> imu) | |
extrinsicTrans: [0.048, 0.0, -0.101] # values in meters | |
extrinsicRot: [0, 0, 1, | |
0, -1, 0, | |
1, 0, 0] | |
extrinsicRPY: [0, 0, 1, | |
0, -1, 0, | |
1, 0, 0] | |
# LOAM feature threshold | |
edgeThreshold: 1.0 | |
surfThreshold: 0.1 | |
edgeFeatureMinValidNum: 10 # default 10 | |
surfFeatureMinValidNum: 100 # default 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: 1000 # meters | |
rotation_tollerance: 1000 # radians | |
# CPU Params | |
numberOfCores: 7 # 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 | |
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 | |
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 | |
# 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] |
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