使用开源激光SLAM方案LIO-SAM运行KITTI数据集，如有用，请评论雷锋

第一次写博客~

1. LIOSAM作为优秀的激光slam方案，当然想尝试着运行更多数据啦，然而没有发现类似的方法分享到底如何实现，在B站看到有伙伴发布了测试视频，但是仍然没有写出方法。所以我跑通了之后记录一下
2. 关于代码层面要修改的，因为liosam 要求输入的点云每个点都有ring 信息和相对时间信息，目前的雷达基本具备这些信息啦，但是早期的kitti数据集不具备，所以代码要自己计算一下 ring和time。方法可以参考lego-loam中这部分内容。多说一点，计算的时候每束激光都应该判断halfpassed,而lego-loam中只判断一次，我觉着是有点问题的，如果你不知道我在说什么，就先用lego-loam的方法修改就好了
   
   然后运行 pip3 install tqdm
   python3 kitti2bag.py -t 2011_09_30 -r 0027 raw_synced
3. 配置参数修改 只需要按照readme中修改：
   extrinsicTrans: [-8.086759e-01, 3.195559e-01, -7.997231e-01]
   extrinsicRot: [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]
   extrinsicRPY: [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]
4. 是的 我想到了，您是不是觉着下载kitti再制作很麻烦， 想要我的bag呢，云盘如下：
   请叫我雷锋
   链接: https://pan.baidu.com/s/13MlLlzqUJME8ungRNJbaSQ 密码: hl71
5. 运行结果：
   
   我运行kitti的参数文件（部分）：

CPU Params
  numberOfCores: 4                              # number of cores for mapping optimization
  mappingProcessInterval: 0.15                  # seconds, regulate mapping frequency

  # Topics  for processing kitti by author
  pointCloudTopic: "points_raw"           # Point cloud data/  for kitti  "/kitti/velo/pointcloud"
  imuTopic: "imu_raw"                         # IMU data/ for kitti  "/kitti/oxts/imu"
  # Topics for normal kitti
  #  pointCloudTopic: "kitti/velo/pointcloud"           # Point cloud data/  for kitti  "/kitti/velo/pointcloud"
  #  imuTopic: "kitti/oxts/imu"                         # IMU data/ for kitti  "/kitti/oxts/imu"

  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"
  useGPSInfo: false
  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:
  savePCDDirectory: "/home/zch/00-test/SavedMap"        # in your home folder, starts and ends with "/". Warning: the code deletes "LOAM" folder then recreates it. See "mapOptimization" for implementation
  # Lidar Settings
  #  特别注意： 如果 sensor: velodyne， useCloudRing 和useCloudTime 都为true!! 如果 sensor: kitti useCloudRing 和useCloudTime 都为false!!

  useCloudRing: false
  useCloudTime: false
  sensor: kitti                              # lidar sensor type, either 'velodyne' or 'ouster' or kitti without ring information
  N_SCAN: 64                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  downsampleRate: 2                           # 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: 250.0                       # default: 1000.0, maximum lidar range to be used
  lidar_has_ring: true
  # 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 (lidar -> IMU)  for kitti
  extrinsicTrans: [-8.086759e-01, 3.195559e-01, -7.997231e-01]
  extrinsicRot: [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]
  extrinsicRPY: [9.999976e-01, 7.553071e-04, -2.035826e-03, -7.854027e-04, 9.998898e-01, -1.482298e-02, 2.024406e-03, 1.482454e-02, 9.998881e-01]

  # 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

  # 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)

Original: https://blog.csdn.net/weixin_44528885/article/details/122576884
Author: 我兔会飞
Title: 使用开源激光SLAM方案LIO-SAM运行KITTI数据集，如有用，请评论雷锋