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<div align = "center"><img src="assets/car.png" width="5%" height="5%" /> G3Reg: </div>

<div align = "center">Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model</div>

Zhijian Qiao, Zehuan Yu, Binqian Jiang, Huan Yin, and Shaojie Shen

IEEE Transactions on Automation Science and Engineering

News

03 Apr 2024: Accepted by IEEE TASE!
19 Dec 2023: Conditionally Accept.
22 Aug 2023: We released our paper on Arxiv and submit it to IEEE TASE.

Abstract

<div align="center"><h4>G3Reg is a fast and robust global registration framework for point clouds.</h4></div> <div align = "center"><img src="assets/pipeline.png" width="95%" /> </div>

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.
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.
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

Qualitative results on datasets

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:

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.

@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}}

@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}
}

Awesome

<div align = "center"><img src="assets/car.png" width="5%" height="5%" /> G3Reg: </div>

<div align = "center">Pyramid Graph-based Global Registration using Gaussian Ellipsoid Model</div>

News

Abstract

Getting Started

Qualitative results on datasets

KITTI-08

Apollo-Highway

Apollo-Sunnyvale

Livox-HIT-1

Livox-HIT-3

Application to Multi-session Map Merging

Acknowledgements

Citation