FAST_LIO

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Thanks for sharing your great work! Description: I have tested FAST_LIO on utbmhttps://github.com/epan-utbm/utbm_robocar_dataset dataset recently. Most of time, it produces an accurate odometry. But on the utbm_2019_04_18(Roundabout) data, it suffers a large drift.

My config file velodyne.yaml is as below: common: lid_topic: "/velodyne_points" imu_topic: "/imu/data" time_sync_en: false # ONLY turn on when external time synchronization is really not possible

preprocess: lidar_type: 2 # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, scan_line: 32 blind: 4

mapping: acc_cov: 0.1 gyr_cov: 0.1 b_acc_cov: 0.0001 b_gyr_cov: 0.0001 fov_degree: 180 det_range: 100.0 extrinsic_est_en: false # true: enable the online estimation of IMU-LiDAR extrinsic extrinsic_T: [ -0.5, 1.4, 1.5] extrinsic_R: [ 1, 0, 0, 0, 1, 0, 0, 0, 1]

publish: path_en: false scan_publish_en: true # false: close all the point cloud output dense_publish_en: true # false: low down the points number in a global-frame point clouds scan. scan_bodyframe_pub_en: true # true: output the point cloud scans in IMU-body-frame

pcd_save: pcd_save_en: false interval: -1 # how many LiDAR frames saved in each pcd file; # -1 : all frames will be saved in ONE pcd file, may lead to memory crash when having too much frames.

Did you try extrinsic_T: [ -0.5, 1.4, 1.5]?

@XW-HKU Thank you for your responses! When I run FAST_LIO on the utbm_20190418_roundabout data, everything is normal. There is no yellow warning and the extrinsic_T is [ -0.5, 1.4, 1.5].

What will happens if you set the try extrinsic_T to [ 0, 0, 0.28]?

It also suffers a large drift just like using [ -0.5, 1.4, 1.5].

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