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https://github.com/HKUST-Aerial-Robotics/G3Reg
A fast and robust global registration library for outdoor LiDAR point clouds.
https://github.com/HKUST-Aerial-Robotics/G3Reg
registration
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A fast and robust global registration library for outdoor LiDAR point clouds.
- Host: GitHub
- URL: https://github.com/HKUST-Aerial-Robotics/G3Reg
- Owner: HKUST-Aerial-Robotics
- License: mit
- Created: 2023-08-22T19:10:59.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-07T03:40:32.000Z (6 months ago)
- Last Synced: 2024-09-07T04:10:36.258Z (5 months ago)
- Topics: registration
- Language: C++
- Homepage:
- Size: 13 MB
- Stars: 204
- Watchers: 13
- Forks: 13
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-and-novel-works-in-slam - [Code
README
#
G3Reg:
##
Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model
> [Zhijian Qiao](https://qiaozhijian.github.io/), Zehuan Yu, Binqian Jiang, [Huan Yin](https://huanyin94.github.io/), and [Shaojie Shen](https://uav.hkust.edu.hk/group/)
>
> IEEE Transactions on Automation Science and Engineering
### News
* **`03 Apr 2024`:** Accepted by [IEEE TASE](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8856)!
* **`19 Dec 2023`:** Conditionally Accept.
* **`22 Aug 2023`:** We released our paper on Arxiv and submit it to [IEEE TASE](https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8856).
## Abstract
G3Reg is a fast and robust global registration framework for point clouds.
**Features**:
+ **Fast matching**: We utilize segments, including planes, clusters, and lines, parameterized as Gaussian Ellipsoid Models (GEM) to facilitate registration.
+ **Robustness**: We introduce a distrust-and-verify scheme, termed Pyramid Compatibility Graph for Global Registration (PAGOR), designed to enhance the robustness of the registration process.
+ **Framework Integration**: Both GEM and PAGOR can be integrated into existing registration frameworks to boost their performance.
**Note to Practitioners**:
+ **Application Scope**: The method outlined in this paper focuses on global registration of outdoor LiDAR point clouds. However, the fundamental principles of G3Reg, including segment-based matching and PAGOR, are applicable to any point-based registration tasks, including indoor environments.
+ **Segmentation Check**: If the registration does not perform as expected on your point cloud, it is advisable to review the segmentation results closely, referring to [Segmentation Demo](docs/demo.md).
+ **Alternative Matching Approaches**: For practitioners preferring not to use GEM-based matching, point-based matching is a viable alternative. For implementation details, please refer to the configuration file at [fpfh_pagor](configs/kitti_lc_bm/fpfh_pagor.yaml).
+ **Limitations**: Segment-based matching may be less effective in environments with sparse geometric information, such as areas with dense vegetation. In such scenarios, enhancing segment descriptions through hand-crafted or deep learning-based descriptors is recommended to improve matching accuracy.
## Getting Started
- [Installation](docs/install.md)
- [Demo](docs/demo.md)
- [Benchmarks](docs/benchmarks.md)
## Qualitative results on datasets
### KITTI-08
https://github.com/HKUST-Aerial-Robotics/G3Reg/assets/21232185/8f4091b5-5305-4236-afb6-00ea5799ecd7
### Apollo-Highway
https://github.com/HKUST-Aerial-Robotics/G3Reg/assets/21232185/f1d4c9ad-04e9-4cf4-890a-12714f74eb59
### Apollo-Sunnyvale
https://github.com/HKUST-Aerial-Robotics/G3Reg/assets/21232185/60c7bf50-cd1c-447d-964d-1902e4db0489
### Livox-HIT-1
https://github.com/HKUST-Aerial-Robotics/G3Reg/assets/21232185/ee1d9dd1-d460-4970-b060-ada25bc8e004
### Livox-HIT-3
https://github.com/HKUST-Aerial-Robotics/G3Reg/assets/21232185/ef453f89-c92b-4d26-b232-3db2e3bac3f3
## Application to Multi-session Map Merging
## Acknowledgements
We would like to show our greatest respect to authors of the following repos for making their works public:
* [Teaser](https://github.com/MIT-SPARK/TEASER-plusplus)
* [Segregator](https://github.com/Pamphlett/Segregator)
* [Quatro](https://github.com/url-kaist/Quatro)
* [3D-Registration-with-Maximal-Cliques](https://github.com/zhangxy0517/3D-Registration-with-Maximal-Cliques)
## Citation
If you find G3Reg is useful in your research or applications, please consider giving us a star 🌟 and citing it by the following BibTeX entry.
```bibtex
@ARTICLE{qiao2024g3reg,
author={Qiao, Zhijian and Yu, Zehuan and Jiang, Binqian and Yin, Huan and Shen, Shaojie},
journal={IEEE Transactions on Automation Science and Engineering},
title={G3Reg: Pyramid Graph-Based Global Registration Using Gaussian Ellipsoid Model},
year={2024},
volume={},
number={},
pages={1-17},
keywords={Point cloud compression;Three-dimensional displays;Laser radar;Ellipsoids;Robustness;Upper bound;Uncertainty;Global registration;point cloud;LiDAR;graph theory;robust estimation},
doi={10.1109/TASE.2024.3394519}}
```
```bibtex
@inproceedings{qiao2023pyramid,
title={Pyramid Semantic Graph-based Global Point Cloud Registration with Low Overlap},
author={Qiao, Zhijian and Yu, Zehuan and Yin, Huan and Shen, Shaojie},
booktitle={2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages={11202--11209},
year={2023},
organization={IEEE}
}
```