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TixiaoShan / LIO-SAM

LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
BSD 3-Clause "New" or "Revised" License
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Why are there time differences even after the ptp synchronization for Ouster Lidar and the Xsens IMU #454

Open engineer0900 opened 1 year ago

engineer0900 commented 1 year ago

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 # 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: 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!

PranavShevkar commented 9 months ago

Did you manage to solve the issue? If so can you tell me how. Thanks