Why are there time differences even after the ptp synchronization for Ouster Lidar and the Xsens IMU
engineer0900 opened this issue · 1 comments
Hey there,
For my ouster Lidar os1 - 64, and Xsens Mti-680 G. Lio - sam has been setup successfully. The problem which occurs with my lio sam is even after using ptpd synchronization there is still time difference between the ouster lidar and the xsens Imu. The details of the timestamps are as follows.
For the IMU -
rostopic echo /imu/data/header/stamp
secs: 1696934867
nsecs: 493420447
secs: 1696934867
nsecs: 493469791
secs: 1696934867
nsecs: 493500406
secs: 1696934867
nsecs: 493528903
secs: 1696934867
nsecs: 536161880
secs: 1696934867
nsecs: 536211368
For The ouster lidar -
rostopic echo /ouster/points/header/stamp
secs: 1696934920
nsecs: 209591040
secs: 1696934920
nsecs: 309561600
secs: 1696934920
nsecs: 409493760
secs: 1696934920
nsecs: 509417472
secs: 1696934920
nsecs: 609329920
secs: 1696934920
nsecs: 709219328
secs: 1696934920
nsecs: 809160960
secs: 1696934920
nsecs: 909127168
The command I had used is as follows - sudo ptpd -i enp2s0 -M.
This makes the imu drift everytime with ros warning - "Large velocity, reset IMU-preintegration!". My Params.yaml file has following parameters.
lio_sam:
Topics
pointCloudTopic: "ouster/points" # Point cloud data
imuTopic: "/imu/data" # IMU data
odomTopic: "odometry/imu" # IMU pre-preintegration odometry, same frequency as IMU
gpsTopic: "gps/xsens" # GPS odometry topic from navsat, see module_navsat.launch file
Frames
lidarFrame: "os_lidar"
baselinkFrame: "base_link"
odometryFrame: "odom"
mapFrame: "map"
GPS Settings
useImuHeadingInitialization: true # 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 # #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: ouster # lidar sensor type, 'velodyne' or 'ouster' or 'livox'
N_SCAN: 64 # number of lidar channel (i.e., Velodyne/Ouster: 16, 32, 64, 128, Livox Horizon: 6)
Horizon_SCAN: 1024 # 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: 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: [0.0, 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,
1.0, 0.0, 0.0,
0.0, 0.0, 1.0]
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, 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 # meters
rotation_tollerance: 1000 # 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
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]
Could you please suggest any of the solutions for this error. Thankyou!
Did you manage to solve the issue? If so can you tell me how. Thanks