yuyuyu00 / LeGO-LOAM

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

LeGO-LOAM

This repository contains code for a lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for ROS compatible UGVs. The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontal) and optional IMU data as inputs. It outputs 6D pose estimation in real-time. A demonstration of the system can be found here -> https://www.youtube.com/watch?v=O3tz_ftHV48

Dependency

• ROS (tested with indigo and kinetic)
• gtsam (Georgia Tech Smoothing and Mapping library)

Compile

You can use the following commands to download and compile the package.

cd ~/catkin_ws/src
git clone https://github.com/RobustFieldAutonomyLab/LeGO-LOAM.git
cd ..
catkin_make -j1

When you compile the code for the first time, you need to add "-j1" behind "catkin_make" for generating some message types. "-j1" is not needed for future compiling.

Run the package

1. 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.

2. 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

Cite LeGO-LOAM

Thank you for citing our LeGO-LOAM paper if you use any of this code:

@inproceedings{legoloam2018,
  title={LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain},
  author={Dub{\'e}, Tixiao Shan and Brendan Englot},
  booktitle={IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={Accepted, To Appear in October},
  year={2018},
  organization={IEEE}
}