LIO-SAM
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TixiaoShan
Reconstruct jump and error
Hello, I'm new to try LIO-SAM by my own data. Initially my Lidar TF drift a little but reconstruct doesn't failed (Really god bless). But after my AMR go straight it will jump and then fail reconstruct. Below shared the picture of my rviz and bag data.
Here is the some my data describe:
- Lidar : Velodyne VLP-16 Puck
- IMU : YAHboom 10 axis IMU
- Location: Indoor corridor
- ROS2 foxy
- data from AMR go straight and back straight
the problem picture :
the params.yaml:
ros__parameters:
# Topics
pointCloudTopic: "/points2" # Point cloud data
imuTopic: "/imu" # 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: "velodyne" #default : "lidar_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: "/home/droxo/LIO-SAM-ros2/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: velodyne # lidar sensor type, either 'velodyne', 'ouster' or 'livox'
N_SCAN: 16 # number of lidar channels (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: 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: 1000.0 # 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
extrinsicTrans: [ 0.0, 0.0, 0.0 ]
extrinsicRot: [1.0, 0.0, 0.0,
0.0, 1.0, 0.0,
0.0, 0.0,1.0 ]
extrinsicRPY: [1.0, 0.0, 0.0,
0.0, -0.259, 0.966,
0.0, -0.966, -0.259 ] #degree -105
# 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: 1000.0 # meters
rotation_tollerance: 1000.0 # 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
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
ROS2 bag data include lidar data % imu data
ROS2 bag : https://drive.google.com/file/d/1mcWyffvXN4X3OiJ3ntP_UOecvU6WCeod/view?usp=sharing lidar data : /points imu data : /imu
I have try change extrinsicRot and extrinsicRPY, and I have get a perfact model once. But only once. It was very tricky. Could anyone give me advise and if I have new finding update here. Thanks.
Hello, can I communicate with you about running lio-sam with ROS2? How can I get in touch with you?
Hello, can I communicate with you about running lio-sam with ROS2? How can I get in touch with you?
Sure. Could you give me your email? I could send you email in private.
Same problem here under ros2-humble. Do you have found the reason for that behaviour?
@lukasj-imar Unfortunately no. But I change to use other person's open source which based on LIO-SAM. I think the problem comes from IMU. But I didn't know the reason.
@lukasj-imar Unfortunately no. But I change to use other person's open source which based on LIO-SAM. I think the problem comes from IMU. But I didn't know the reason.
可以解决这个问题嘛，那个开源的基于lio-sam的，如果可以的话，可以分享一下嘛
@lukasj-imar Sure. You could check li-slam-ros2 https://github.com/rsasaki0109/li_slam_ros2
I found a solution for my problem, check: https://github.com/TixiaoShan/LIO-SAM/issues/437