Tf error phenomenon when using Gazebo

[Anchangun]

Hello

I'm conducting tests using turtlebot and velodyne simulator, but the tf moves automatically. I think there might be something wrong with the imu settings, and I need help.

The parameters are as follows

ros__parameters:

# Topics
pointCloudTopic: "/velodyne_points"                   # 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: "base_scan"
baselinkFrame: "base_footprint"
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: true                               # 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: 1.0e-2
imuGyrNoise: 1.0e-2
imuAccBiasN: 1.0e-4
imuGyrBiasN: 1.0e-4
imuGravity: 9.81
imuRPYWeight: 0.01

extrinsicTrans:  [-2.155, 0.0, -0.83]
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,  1.0,  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: 0.4                     # default: 0.4 - outdoor, 0.2 - indoor
mappingCornerLeafSize: 0.2                    # default: 0.2 - outdoor, 0.1 - indoor
mappingSurfLeafSize: 0.4                      # 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: 0.4                  # 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

[ibra860]

I also got the same phenomenon I am doing a simulation for a UAV. Did you find a way to fix the it?

[ZhongmouLi]

I am having this issue now. Do you have any solution?