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Welcome to the LeGO_LOAM_Pole wiki!

This depends on LeGO-LOAM. https://github.com/RobustFieldAutonomyLab/LeGO-LOAM

Dependency

ROS (tested with indigo, kinetic, and melodic)
gtsam (Georgia Tech Smoothing and Mapping library, 4.1.1)

wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.1.1
cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
cd ~/Downloads/gtsam-4.1.l/
mkdir build && cd build
cmake ..
sudo make install

Compile

cd ~/catkin_ws/src   
git clone https://github.com/VIPLabGachon/LeGO_LOAM_Pole.git    
cd ..    
catkin_make -j1

New Lidar

The key thing to adapt the code to a new sensor is making sure the point cloud can be properly projected to an range image and ground can be correctly detected. For example, VLP-16 has a angular resolution of 0.2° and 2° along two directions. It has 16 beams. The angle of the bottom beam is -15°. Thus, the parameters in "utility.h" are listed as below. When you implement new sensor, make sure that the ground_cloud has enough points for matching. Before you post any issues, please read this.

extern const int N_SCAN = 16;
extern const int Horizon_SCAN = 1800;
extern const float ang_res_x = 0.2;
extern const float ang_res_y = 2.0;
extern const float ang_bottom = 15.0;
extern const int groundScanInd = 7;

Another example for Velodyne HDL-32e range image projection:

extern const int N_SCAN = 32;
extern const int Horizon_SCAN = 1800;
extern const float ang_res_x = 360.0/Horizon_SCAN;
extern const float ang_res_y = 41.333/float(N_Scan-1);
extern const float ang_bottom = 30.666666;
extern const int groundScanInd = 20;

New: a new useCloudRing flag has been added to help with point cloud projection (i.e., VLP-32C, VLS-128). Velodyne point cloud has "ring" channel that directly gives the point row id in a range image. Other lidars may have a same type of channel, i.e., "r" in Ouster. If you are using a non-Velodyne lidar but it has a similar "ring" channel, you can change the PointXYZIR definition in utility.h and the corresponding code in imageProjection.cpp.

For KITTI users, if you want to use our algorithm with HDL-64e, you need to write your own implementation for such projection. If the point cloud is not projected properly, you will lose many points and performance.

If you are using your lidar with an IMU, make sure your IMU is aligned properly with the lidar. The algorithm uses IMU data to correct the point cloud distortion that is cause by sensor motion. If the IMU is not aligned properly, the usage of IMU data will deteriorate the result. Ouster lidar IMU is not supported in the package as LeGO-LOAM needs a 9-DOF IMU.

Run the package

Run the launch file:

roslaunch lego_loam run.launch

Notes: The parameter "/use_sim_time" is set to "true" for simulation, "false" to real robot usage.

Play existing bag files:

rosbag play *.bag --clock --topic /velodyne_points /imu/data

Notes: Though /imu/data is optinal, it can improve estimation accuracy greatly if provided. Some sample bags can be downloaded from here.

Loop Closure

The loop-closure method implemented in this package is a naive ICP-based method. It often fails when the odometry drift is too large. For more advanced loop-closure methods, there is a package called SC-LeGO-LOAM, which features utilizing point cloud descriptor.

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