Large velocity detected, triggering IMU-preintegration reset --- Livox Horizon Lidar

[venom0619]

@YJZLuckyBoy
I encountered a warning message in my ROS system with the following content:

Large velocity detected, triggering IMU-preintegration reset and unable to build proper map of the environment

1. Run the following launch commands:

   • roslaunch liorf run_lio_sam_livox.launch
   • roslaunch livox_ros_driver livox_lidar_msg.launch

2. Modify the lio_sam_livox.yaml configuration file as follows:

   • Set imuTopic to /livox/imu instead of imu_raw.
   • Use pointCloudTopic: "points_raw" for the pointCloudTopic.

Environment:

• ROS distribution/version: [ROS1 , noetic]
• Operating System: [Ubuntu 20.04]
• using Livox Horizon Lidar

Can someone help me resolve this issue?

Param File:

liorf:

Topics

pointCloudTopic: "points_raw" # Point cloud data
imuTopic: "/livox/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_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 # TixiaoShan/LIO-SAM#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' or 'robosense'
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(line). i.e., 16 = 64 / 4, 16 = 16 / 1
point_filter_num: 3 # default: 3. Downsample your data if too many points(point). e.g., 16: 1, 32: 5, 64: 8
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

imuType: 0 # 0: 6-axis 1: 9-axis
imuRate: 200.0
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: [0.0, 0.0, 0.0]
extrinsicRot: [-1, 0, 0,
0, 1, 0,
0, 0, -1]

This parameter is set only when the 9-axis IMU is used, but it must be a high-precision IMU. e.g. MTI-680

extrinsicRPY: [0, -1, 0,
1, 0, 0,
0, 0, 1]

voxel filter paprams

mappingSurfLeafSize: 0.15 # 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: 4 # number of cores for mapping optimization
mappingProcessInterval: 0.0 # seconds, regulate mapping frequency

Surrounding map

surroundingkeyframeAddingDistThreshold: 0.5 # 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)
surroundingKeyframeMapLeafSize: 0.3 # downsample local map point cloud

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
loopClosureICPSurfLeafSize: 0.3 # downsample icp point cloud
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]

[lytkinsa96]

The pattern indicates that system fails to initialize (see step "0" in imuPreintegration in odometryHandler).

Try to adjust the priorVelNoise variable.

Also check TixiaoShan/LIO-SAM#104 -- this may help

I encountered a warning message in my ROS system with the following content:

Large velocity detected, triggering IMU-preintegration reset and unable to build proper map of the environment

1. Run the following launch commands:
   
   * `roslaunch liorf run_lio_sam_livox.launch`
   * `roslaunch livox_ros_driver livox_lidar_msg.launch`

2. Modify the `lio_sam_livox.yaml` configuration file as follows:
   
   * Set `imuTopic` to `/livox/imu` instead of `imu_raw`.
   * Use `pointCloudTopic: "points_raw"` for the `pointCloudTopic`.

Environment:

* ROS distribution/version: [ROS1 , noetic]

* Operating System: [Ubuntu 20.04]

* using Livox Horizon Lidar

Can someone help me resolve this issue?

[venom0619]

@lytkinsa96

Thanks for the reply can you be more specific on adjusting the priorVelNoise variable.