@@ -84,7 +84,11 @@ The user needs to prepare the point cloud data in the correct format for cloud d
-**IMU requirement**. Like the original LOAM implementation, LIO-SAM only works with a 9-axis IMU, which gives roll, pitch, and yaw estimation. The roll and pitch estimation is mainly used to initialize the system at the correct attitude. The yaw estimation initializes the system at the right heading when using GPS data. Theoretically, an initialization procedure like VINS-Mono will enable LIO-SAM to work with a 6-axis IMU. The performance of the system largely depends on the quality of the IMU measurements. The higher the IMU data rate, the better the system accuracy. We use Microstrain 3DM-GX5-25, which outputs data at 500Hz. We recommend using an IMU that gives at least a 200Hz output rate. Note that the internal IMU of Ouster lidar is not usable due to high vibration.
-**IMU alignment**. LIO-SAM transforms IMU raw data from the IMU frame to the Lidar frame, which follows the ROS REP-105 convention (x - forward, y - left, z - upward). To make the system function properly, the correct extrinsic transformation needs to be provided in "params.yaml" file. Using our setup as an example, we need to set the readings of x-z acceleration and gyro negative to transform the IMU data in the lidar frame, which is indicated by "extrinsicRot" in "params.yaml." The transformation of attitude readings is similar. We rotate the attitude measurements by -90 degrees around "lidar-z" axis and get the corresponding readings in the lidar frame. This transformation is indicated by "extrinsicRPY" in "params.yaml."
-**IMU alignment**. LIO-SAM transforms IMU raw data from the IMU frame to the Lidar frame, which follows the ROS REP-105 convention (x - forward, y - left, z - upward). To make the system function properly, the correct extrinsic transformation needs to be provided in "params.yaml" file.
Using our setup as an example:
- we need to set the readings of x-z acceleration and gyro negative to transform the IMU data in the lidar frame, which is indicated by "extrinsicRot" in "params.yaml."
- 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. This transformation is indicated by "extrinsicRPY" in "params.yaml."
-**IMU debug**. It's strongly recommended that the user uncomment the debug lines in "imuHandler()" of "imageProjection.cpp" and test the output of the transformed IMU data. The user can rotate the sensor suite to check whether the readings correspond to the sensor's movement.
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@@ -119,7 +123,7 @@ rosbag play your-bag.bag -r 3
## Paper
Thank you for citing [LIO-SAM](./config/doc/paper.pdf) if you use any of this code.
Thank you for citing [LIO-SAM (IROS-2020)](./config/doc/paper.pdf) if you use any of this code.
```
@inproceedings{liosam2020shan,
title={LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping},
