LeGO-LOAM

Introduction

LeGO-LOAM is a lightweight and ground-optimized lidar odometry and mapping method, LeGO-LOAM, for realtime six degree-of-freedom pose estimation with ground vehicles. LeGO-LOAM is lightweight, as it can achieve realtime pose estimation on a low-power embedded system. LeGOLOAM is ground-optimized, as it leverages the presence of a ground plane in its segmentation and optimization steps. It first apply point cloud segmentation to filter out noise, and feature extraction to obtain distinctive planar and edge features. A two-step Levenberg-Marquardt optimization method then uses the planar and edge features to solve different components of the six degree-of-freedom transformation across consecutive scans. Here are some reference links: code link. paper link.

Image description — Fig. 1. Workflow diagram of LeGO-LOAM

2 Compilation Process

2.1 Requirement

ubuntu20.04 、ros-noetic、cmake、gcc、g++、eigen、pcl1.10、gtsam、code、bag
During the installation of third-party libraries, there may be many version mismatch issues.
Below are just examples of installation.
Please make sure to match the versions, otherwise compilation will have significant problems.
If there are any issues during the appeal process, please search online to resolve them.

install dependence


                  sudo apt-get update 

                  sudo apt-get install git gcc g++ vim make cmake

install Eigen3


                  download Eigen3 

                  cd eigen3

                  mkdir build && cd build

                  cmake ..

                  make

                  sudo make install

install gtsam


                  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 ..

                  make

                  sudo make install

2.2 Build LeGO-LOAM

Clone the repository and catkin_make:


            cd ~/catkin_ws/src

            git clone https://github.com/RobustFieldAutonomyLab/LeGO-LOAM.git 

            cd ../

            catkin_make

            source ~/catkin_ws/devel/setup.bash

2.3 Run LeGO-LOAM


            roslaunch lego_loam run.launch

            播放数据集，虽然/imu/data 是可选的，但如果提供它可以大大提高估计精度：

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