https://github.com/koide3/direct_visual_lidar_calibration

A toolbox for target-less LiDAR-camera calibration [ROS1/ROS2]
https://github.com/koide3/direct_visual_lidar_calibration

calibration camera lidar nid ros ros2

Last synced: 2 days ago
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A toolbox for target-less LiDAR-camera calibration [ROS1/ROS2]

• Host: GitHub
• URL: https://github.com/koide3/direct_visual_lidar_calibration
• Owner: koide3
• Created: 2022-09-13T01:06:03.000Z (over 2 years ago)
• Default Branch: main
• Last Pushed: 2024-09-22T00:29:02.000Z (4 months ago)
• Last Synced: 2024-10-30T03:44:39.228Z (3 months ago)
• Topics: calibration, camera, lidar, nid, ros, ros2
• Language: C++
• Homepage: https://koide3.github.io/direct_visual_lidar_calibration/
• Size: 815 KB
• Stars: 742
• Watchers: 18
• Forks: 113
• Open Issues: 45
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-and-novel-works-in-slam - [LiDARCam

README

        
# direct_visual_lidar_calibration

This package provides a toolbox for LiDAR-camera calibration that is: 

- **Generalizable**: It can handle various LiDAR and camera projection models including spinning and non-repetitive scan LiDARs, and pinhole, fisheye, and omnidirectional projection cameras.

- **Target-less**: It does not require a calibration target but uses the environment structure and texture for calibration.

- **Single-shot**: At a minimum, only one pairing of a LiDAR point cloud and a camera image is required for calibration. Optionally, multiple LiDAR-camera data pairs can be used for improving the accuracy.

- **Automatic**: The calibration process is automatic and does not require an initial guess.

- **Accurate and robust**: It employs a pixel-level direct LiDAR-camera registration algorithm that is more robust and accurate compared to edge-based indirect LiDAR-camera registration.

**Documentation: [https://koide3.github.io/direct_visual_lidar_calibration/](https://koide3.github.io/direct_visual_lidar_calibration/)**  

**Docker hub: [koide3/direct_visual_lidar_calibration](https://hub.docker.com/repository/docker/koide3/direct_visual_lidar_calibration)**

[![Build](https://github.com/koide3/direct_visual_lidar_calibration/actions/workflows/push.yaml/badge.svg)](https://github.com/koide3/direct_visual_lidar_calibration/actions/workflows/push.yaml) [![Docker Image Size (latest by date)](https://img.shields.io/docker/image-size/koide3/direct_visual_lidar_calibration)](https://hub.docker.com/repository/docker/koide3/direct_visual_lidar_calibration)

![213393920-501f754f-c19f-4bab-af82-76a70d2ec6c6](https://user-images.githubusercontent.com/31344317/213427328-ddf72a71-9aeb-42e8-86a5-9c2ae19890e3.jpg)

[Video](https://www.youtube.com/watch?v=7TM7wGthinc&feature=youtu.be)

## Dependencies

- [ROS1/ROS2](https://www.ros.org/)

- [PCL](https://pointclouds.org/)

- [OpenCV](https://opencv.org/)

- [GTSAM](https://gtsam.org/)

- [Ceres](http://ceres-solver.org/)

- [Iridescence](https://github.com/koide3/iridescence)

- [SuperGlue](https://github.com/magicleap/SuperGluePretrainedNetwork) [optional]

## Getting started

1. [Installation](https://koide3.github.io/direct_visual_lidar_calibration/installation/) / [Docker images](https://koide3.github.io/direct_visual_lidar_calibration/docker/)

2. [Data collection](https://koide3.github.io/direct_visual_lidar_calibration/collection/)

3. [Calibration example](https://koide3.github.io/direct_visual_lidar_calibration/example/)

4. [Program details](https://koide3.github.io/direct_visual_lidar_calibration/programs/)

## License

This package is released under the MIT license.

## Publication

Koide et al., General, Single-shot, Target-less, and Automatic LiDAR-Camera Extrinsic Calibration Toolbox, ICRA2023, [[PDF]](https://staff.aist.go.jp/k.koide/assets/pdf/icra2023.pdf)

## Contact

Kenji Koide, National Institute of Advanced Industrial Science and Technology (AIST), Japan