FAST-LIO 跑自制数据

FAST-LIO 跑自制数据

官方 GitHub 给了非常详细的指导，按照步骤来即可

https://github.com/hku-mars/FAST_LIO

首先需要用 LI-Init 完成激光雷达与 IMU 的时空标定，然后填入 YAML 配置文件即可

common:
    lid_topic:  "/velodyne_points"
    imu_topic:  "/zedm/zed_node/imu/data_raw"
    time_sync_en: true         # ONLY turn on when external time synchronization is really not possible
    time_offset_lidar_to_imu: 0.127214  # Time offset between lidar and IMU calibrated by other algorithms, e.g. LI-Init (can be found in README).
                                        # This param will take effect no matter what time_sync_en is. So if the time offset is not known exactly, please set as 0.0

preprocess:
    lidar_type: 2                # 1 for Livox serials LiDAR, 2 for Velodyne LiDAR, 3 for ouster LiDAR, 
    scan_line: 16
    scan_rate: 10                # only need to be set for velodyne, unit: Hz,
    timestamp_unit: 2            # the unit of time/t field in the PointCloud2 rostopic: 0-second, 1-milisecond, 2-microsecond, 3-nanosecond.
    blind: 2

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: [ -0.068703, -0.094532, 0.067275]
    extrinsic_R: [ 0.999895, -0.007797, 0.012203, 
                   0.007883, 0.999944, -0.007073, 
                   -0.012147, 0.007168, 0.999901]

publish:
    path_en: true
    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: true
    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.

建图效果尚可