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hi, I'm using ros2 humble. I want to do a fusion with the velodyne VLP-16 and the IMU of the Zed camera. I have installed your LIOSAM package and edited the params.yaml file. But there is a problem with the rviz. Lidar and zed are not completely moving together. There is a deviation in my point dataPoint cloud data is also getting into each other. How do I solve this problem?
I run velodyne ,zed and liosam on terminal.
here is my params.yaml file
`/**:
ros__parameters:
# Topics
pointCloudTopic: "/velodyne_points" # Point cloud data
imuTopic: "/zed/zed_node/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"
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: 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: [ 0.0, 1.0, 0.0,
# -1.0, 0.0, 0.0,
# 0.0, 0.0, 1.0 ]
# 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`
The text was updated successfully, but these errors were encountered:
I just wanted to open an Issue as well because my rviz seems to behave similar to yours. I also using an VLP16 but with an BNO055 and using iron and not humble. I checked a view things and found out that my Tree looks not as expected when I try to run lio-sam with online data. When everything is up and running my tree looks like this
This seems to be wrong as the urdf file describes that lidar_link should be connected to base_link and not to odom.
I see an error message when I launch lio_sam which might cause this behavior.
[lio_sam_imuPreintegration-2] [ERROR] [1733499270.452512516] [lio_sam_imuPreintegration]: Could not find a connection between 'lidar_link' and 'base_link' because they are not part of the same tree.Tf has two or more unconnected trees.
I dont know how to solve that right now as I'm a ros noob.but as we might facing similar Problems could you please check ros2 run tf2_tools view_framesin a another terminal while running lio_sam and check the generated pdf afterwards?
hi, I'm using ros2 humble. I want to do a fusion with the velodyne VLP-16 and the IMU of the Zed camera. I have installed your LIOSAM package and edited the params.yaml file. But there is a problem with the rviz. Lidar and zed are not completely moving together. There is a deviation in my point dataPoint cloud data is also getting into each other. How do I solve this problem?
I run velodyne ,zed and liosam on terminal.
here is my params.yaml file
`/**:
ros__parameters:
The text was updated successfully, but these errors were encountered: