https://github.com/hyye/lio-mapping

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)
https://github.com/hyye/lio-mapping

icra2019 mapping sensor-fusion slam

Last synced: about 1 month ago
JSON representation

Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping)

• Host: GitHub
• URL: https://github.com/hyye/lio-mapping
• Owner: hyye
• License: gpl-3.0
• Created: 2018-09-17T04:17:07.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2020-02-13T20:05:15.000Z (over 4 years ago)
• Last Synced: 2024-02-04T14:34:23.191Z (4 months ago)
• Topics: icra2019, mapping, sensor-fusion, slam
• Language: C++
• Homepage: https://sites.google.com/view/lio-mapping
• Size: 827 KB
• Stars: 861
• Watchers: 39
• Forks: 320
• Open Issues: 35
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• Awesome-Self-Driving - lio-mapping - Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping). (8. SLAM / 8.1. Lidar-based)
• awesome-robotic-tooling - lio-mapping - Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping). (Simultaneous Localization and Mapping / Lidar)
• Awesome-SLAM - LIO-mapping: A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach
• awesome-robotic-tooling - lio-mapping - Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping) (Localization / SLAM)
• awesome-robotic-tooling - lio-mapping - Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping). (Simultaneous Localization and Mapping / Lidar)
• awesome-stars - hyye/lio-mapping - Implementation of Tightly Coupled 3D Lidar Inertial Odometry and Mapping (LIO-mapping) (C++)

README

        
# LIO-mapping

## A Tightly Coupled 3D Lidar and Inertial Odometry and Mapping Approach

#### Authors: Haoyang Ye, Yuying Chen, and Ming Liu from [RAM-LAB](https://ram-lab.com/).

[[Paper]](https://arxiv.org/abs/1904.06993), [[Project]](https://sites.google.com/view/lio-mapping), [[Bib]](https://ram-lab.com/papers/2019/icra_2019_ye.bib). ICRA 2019.

## Table of Contents

* [Demo Results](#demo-results)

* [Prerequisites](#prerequisites)

* [Build](#build)

* [Examples](#examples)

* [Docker](#docker)

* [Credits](#credits)

* [Licence](#licence)

## Demo Results





Video: [[More indoor and outdoor tests]](https://ram-lab.com/file/hyye/lio-mapping.mp4).

## Prerequisites

See [Dockerfile](docker/Dockerfile) as a reference:

1. [ROS](http://wiki.ros.org/melodic/Installation) with Ubuntu 18.04 or Ubuntu 16.04.

2. [Ceres-solver](http://ceres-solver.org/installation.html#linux).

3. [PCL](http://www.pointclouds.org/downloads/), the default version accompanying by ROS.

4. [OpenCV](https://docs.opencv.org/master/d7/d9f/tutorial_linux_install.html), the default version accompanying by ROS.

## Build

1. `git clone [email protected]:hyye/lio-mapping.git` into the `src` folder of your catkin workspace.

2. `catkin build -DCMAKE_BUILD_TYPE=Release lio` or `catkin_make -DCMAKE_BUILD_TYPE=Release`.

## Examples

Some [sample data](https://drive.google.com/drive/folders/1dPy667dAnJy9wgXmlnRgQZxQF_ESuve3).

1. `source devel/setup.zsh`, or `setup.bash` if your prefer `bash`.

2. `roslaunch lio test_indoor.launch &`.

3. `roslaunch lio map_4D_indoor.launch &`.

4. `rosbag play fast1.bag`.

## Docker

Try it out using docker:

1. Run `docker/build_docker.sh`.

2. Run `docker/run_docker.sh`.

3. Run `rosbag play fast1.bag`, in your host machine or in the running container.

Note: Visualization (rviz) can run in the running container with [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). The [Dockerfile](docker/Dockerfile) is compatible with [nvidia-docker 2.0](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)); [1.Dockerfile](docker/1.Dockerfile) with [nvidia-docker 1.0](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-1.0)).

## Credits

The feature extraction, lidar-only odometry and baseline implemented were heavily derived or taken from the original [LOAM](http://wiki.ros.org/loam_velodyne) and its [modified version](https://github.com/laboshinl/loam_velodyne) (the point_processor in our project), and one of the initialization methods and the optimization pipeline from [VINS-mono](https://github.com/HKUST-Aerial-Robotics/VINS-Mono). The copyright headers are retained for the relevant files.

## Licence

The source code is released under [GPL-3.0](https://www.gnu.org/licenses/).