About the transform from imu to lidar for other datasets

[zwmisdu]

Thank you for your work again! When I use the dataset in https://github.com/TixiaoShan/Stevens-VLP16-Dataset or my own dataset, the map will be in a chaotic state shoortly after the beginning. Just as the picture shows from the bag(2018-0518-14-49-12_0.bag)

I have checked the output of odometry and found that the data is wrong. And for my own data, I have known the relative position between imu and lidar, but after modifying the extrinsicRot and extrinsicRPY following the introduction in Readme, I still can not make LIO-SAM run properly. How does the following sentence mean ? "The transformation of attitude readings is slightly different. We rotate the attitude measurements by -90 degrees around "lidar-z" axis and get the corresponding roll, pitch, and yaw readings in the lidar frame."

[zwmisdu]

After reading the pinned issue, I have figured out something.So about theh dataset in https://github.com/TixiaoShan/Stevens-VLP16-Datase, how to get the correct params?

[TixiaoShan]

Did you change the extrinsic matrix to identity matrix?

[zwmisdu]

Did you change the extrinsic matrix to identity matrix?

Yes, with the config now I can run west.bag correctly, but the dataset from Stevens-VLP16-Dataset can not be applied (the names of the topics have been renamed to /points_raw and /imu_raw)

[TixiaoShan]

Try these settings:

lio_sam:

  # Topics
  pointCloudTopic: "velodyne_points"               # Point cloud data
  imuTopic: "imu/data"                         # IMU data
  odomTopic: "odometry/imu"                   # IMU pre-preintegration odometry, same frequency as IMU
  gpsTopic: "odometry/gpsz"                   # GPS odometry topic from navsat, see module_navsat.launch file

  # 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
  N_SCAN: 16                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  timeField: "time"                           # point timestamp field, Velodyne - "time", Ouster - "t"
  downsampleRate: 1                           # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1 

  # IMU Settings
  imuAccNoise: 3.9939570888238808e-03
  imuGyrNoise: 1.5636343949698187e-03
  imuAccBiasN: 6.4356659353532566e-05
  imuGyrBiasN: 3.5640318696367613e-05
  imuGravity: 9.80511

  # Extrinsics (lidar -> IMU)
  extrinsicTrans: [0.0, 0.0, 0.0]
  # extrinsicRot: [-1, 0, 0,
  #                 0, 1, 0,
  #                 0, 0, -1]
  # extrinsicRPY: [0,  1, 0,
  #                -1, 0, 0,
  #                 0, 0, 1]
  extrinsicRot: [1, 0, 0,
                  0, 1, 0,
                  0, 0, 1]
  extrinsicRPY: [1, 0, 0,
                  0, 1, 0,
                  0, 0, 1]

rosbag play 2018-05-18-14-49-12_0.bag --topic /velodyne_points /imu/data

[zwmisdu]

Try these settings:

lio_sam:

  # Topics
  pointCloudTopic: "velodyne_points"               # Point cloud data
  imuTopic: "imu/data"                         # IMU data
  odomTopic: "odometry/imu"                   # IMU pre-preintegration odometry, same frequency as IMU
  gpsTopic: "odometry/gpsz"                   # GPS odometry topic from navsat, see module_navsat.launch file

  # 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
  N_SCAN: 16                                  # number of lidar channel (i.e., 16, 32, 64, 128)
  Horizon_SCAN: 1800                          # lidar horizontal resolution (Velodyne:1800, Ouster:512,1024,2048)
  timeField: "time"                           # point timestamp field, Velodyne - "time", Ouster - "t"
  downsampleRate: 1                           # default: 1. Downsample your data if too many points. i.e., 16 = 64 / 4, 16 = 16 / 1 

  # IMU Settings
  imuAccNoise: 3.9939570888238808e-03
  imuGyrNoise: 1.5636343949698187e-03
  imuAccBiasN: 6.4356659353532566e-05
  imuGyrBiasN: 3.5640318696367613e-05
  imuGravity: 9.80511

  # Extrinsics (lidar -> IMU)
  extrinsicTrans: [0.0, 0.0, 0.0]
  # extrinsicRot: [-1, 0, 0,
  #                 0, 1, 0,
  #                 0, 0, -1]
  # extrinsicRPY: [0,  1, 0,
  #                -1, 0, 0,
  #                 0, 0, 1]
  extrinsicRot: [1, 0, 0,
                  0, 1, 0,
                  0, 0, 1]
  extrinsicRPY: [1, 0, 0,
                  0, 1, 0,
                  0, 0, 1]

rosbag play 2018-05-18-14-49-12_0.bag --topic /velodyne_points /imu/data

Thanks and I rebuild the workspace with the lastest source code, it works.