This repository contains code for a lightweight and ground optimized lidar odometry and mapping (LeGO-LOAM) system for Autonomous Vehicles. The system takes in point cloud from a Velodyne VLP-16 Lidar (palced horizontal) and IMU data as inputs. It outputs 6D pose estimation in real-time.

The easiest way to run this package is by using the provided docker image. Needs NVIDIA GPU.

• Install docker-ce using the instructions given below.

  sudo apt-get update
  
  # to uninstall older versions of docker
  sudo apt-get remove docker docker-engine docker.io
  
  sudo apt-get update
  
  sudo apt-get install \
       apt-transport-https \
       ca-certificates \
       curl \
       software-properties-common
  
  curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
  
  sudo apt-key fingerprint 0EBFCD88
  #Verify that you now have the key with the fingerprint 9DC8 5822 9FC7 DD38 854A E2D8 8D81 803C 0EBF CD88, by searching 	  	   the last 8 characters of the fingerprint.
  
  sudo add-apt-repository \
       "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
       $(lsb_release -cs) \
       stable"
  
  sudo apt-get update
  
  sudo apt-get install docker-ce

• Install nvidia-docker2 plugin. Check the link for its dependencies.

  curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
  
  distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
  
  curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
  
  curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
  
  sudo apt-get update
  # purge older version of nvidia-docker
  sudo apt-get purge nvidia-docker
  
  sudo apt-get install nvidia-docker2
  sudo pkill -SIGHUP dockerd

Terminal 1

git clone https://github.com/ayushgargdroid/lego-loam.git
roscore

Terminal 2

cd lego-loam/docker_files/
./run_script.bash

Terminal 3 (Play the morning rosbag)

rosbag play morning_stereo_rgb_ir_lidar_gps.bag --clock --topics /ns1/velodyne_points /vehicle/gps/fix /camera_array/cam0/camera_info /camera_array/cam0/image_raw /imu/imu

Sit back and relax. Your welcome!

If you are having issues with Docker permissions, please follow this link.

We assume you have ROS Melodic installed and the environment setup.

• ROS
• libpcl>1.8

  sudo apt-get update
  sudo apt-get install libpcl-*1.8

• gtsam (Georgia Tech Smoothing and Mapping library, 4.0.0-alpha2)

  wget -O ~/Downloads/gtsam.zip https://github.com/borglab/gtsam/archive/4.0.0-alpha2.zip
  cd ~/Downloads/ && unzip gtsam.zip -d ~/Downloads/
  cd ~/Downloads/gtsam-4.0.0-alpha2/
  mkdir build && cd build
  cmake -D GTSAM_WITH_EIGEN_MKL=OFF -D CMAKE_CXX_FLAGS="-w" ..
  sudo make install -j8

• GeographicLib (ver 1.50.1) We have a copy of it in the repo directory.

  cd ~/Downloads
  wget https://github.com/ayushgargdroid/lego-loam/blob/master/GeographicLib-1.50.1.tar.gz
  tar -xf GeographicLib-1.50.1.tar.gz
  cd GeographicLib-1.50.1/ && mkdir build
  cmake ..
  sudo make install -j8

• ros-melodic-tf2-sensor-msgs

  sudo apt-get install ros-melodic-tf2-sensor-msgs

• imu_transformer

  cd ~/catkin_ws/src
  git clone https://github.com/ayushgargdroid/imu_transformer.git
  cd ..
  catkin_make
  source devel/setup.bash

• morning_stereo_rgb_ir_lidar_gps.bag dataset

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

cd ~/catkin_ws/src
git clone https://github.com/ayushgargdroid/lego-loam.git
cd ..
catkin_make -j1
source devel/setup.bash

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.

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 strictly like this:

rosbag play morning_stereo_rgb_ir_lidar_gps.bag --clock --topics /ns1/velodyne_points /vehicle/gps/fix /camera_array/cam0/camera_info /camera_array/cam0/image_raw /imu/imu

@inproceedings{behley2019iccv,
  author    = {J. Behley and M. Garbade and A. Milioto and J. Quenzel and S. Behnke and C. Stachniss and J. Gall},
  title     = {{SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences}},
  booktitle = {Proc. of the IEEE/CVF International Conf.~on Computer Vision (ICCV)},
  year      = {2019}
}