QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives

Ji Wu, Huai Yu, Shu Han, Xi-Meng Cai, Ming-Feng Wang, Wen Yang, Gui-Song Xia
CAPTAIN Robotics Group, Wuhan University

[Paper] [Code (TBD)]

Global point cloud registration using QuadricsReg. The raw point clouds are collected by an Unmanned Ground Vehicle (UGV), Unmanned Aerial Vehicle (UAV), and handheld platform equipped with different LiDAR sensors in a roof garden. These point clouds from three sessions are integrated by QuadricsReg.

Abstract

Large-scale point cloud registration is a fundamental problem in robotics, which has significant implications for autonomous navigation, SLAM, and large-scale 3D mapping. In the realm of large-scale point cloud registration, designing a compact symbolic representation is crucial for efficiently processing vast amounts of data, ensuring registration robustness against significant viewpoint variations, occlusions and geometric degeneracy. This paper introduces a novel point cloud registration method, i.e., QuadricsReg, which leverages concise quadrics primitives to represent scenes and utilizes their geometric characteristics to establish correspondences for 6-DoF transformation estimation. As a symbolic feature, the quadric representation fully captures the primary geometric characteristics of scenes, which can efficiently handle the complexity of large-scale point clouds. The intrinsic characteristics of quadrics, such as types and scales, are employed to initialize correspondences. Then we build a multi-level compatibility graph set to find the correspondences using the maximum clique on the geometric consistency between quadrics. Finally, we estimate the 6-DoF transformation using the quadric correspondences, which is further optimized based on the quadric degeneracy-aware distance in a factor graph, ensuring high registration accuracy and robustness against degenerate structures. We test on 5 public datasets and the self-collected heterogeneous dataset across different LiDAR sensors and robot platforms. The exceptional registration success rates and minimal registration errors demonstrate the effectiveness of QuadricsReg in large-scale point cloud registration scenarios. Furthermore, the real-world registration testing on our self-collected heterogeneous dataset shows the robustness and generalization ability of QuadricsReg on different LiDAR sensors and robot platforms.

Scene Representation by Quadrics

Quadric Representation of Pantheon in Italy

Quadric Representation of LiDAR Point Clouds

Registration by QuadricReg

Overview of Registration Results

上一张 下一张

Registration of LiDAR Point Clouds

Real-World Applications

Overview of the Self-Collected Hetero-Reg Dataset

Loop Closure

Multi-session Mapping

References

1. Quatro++: Robust global registration exploiting ground segmentation for loop closing in LiDAR SLAM [paper]
   Lim, Hyungtae and Kim, Beomsoo and Kim, Daebeom and Mason Lee, Eungchang and Myung, Hyun
   The International Journal of Robotics Research, 2024.
2. G3Reg: Pyramid Graph-Based Global Registration Using Gaussian Ellipsoid Model [paper]
   Qiao, Zhijian and Yu, Zehuan and Jiang, Binqian and Yin, Huan and Shen, Shaojie
   IEEE Transactions on Automation Science and Engineering, 2024.
3. QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds [paper]
   Wu, Ji and Yu, Huai and Yang, Wen and Xia, Gui-Song
   International Conference on Robotics and Automation (ICRA), 2024.
4. Unified Representation of Geometric Primitives for Graph-SLAM Optimization Using Decomposed Quadrics [paper]
   Zhen, Weikun and Yu, Huai and Hu, Yaoyu and Scherer, Sebastian
   International Conference on Robotics and Automation (ICRA), 2022.
5. TEASER: Fast and Certifiable Point Cloud Registration [paper]
   Yang, Heng and Shi, Jingnan and Carlone, Luca
   IEEE Transactions on Robotics, 2021.
6. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping [paper]
   Shan, Tixiao and Englot, Brendan and Meyers, Drew and Wang, Wei and Ratti, Carlo and Rus Daniela
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.

BibTeX

@article{QuadricsReg,
  author  = {Ji Wu and Huai Yu and Shu Han and Xi-Meng Cai and Ming-Feng Wang and Wen Yang and Gui-Song Xia},
  title   = {QuadricsReg: Large-Scale Point Cloud Registration using Quadric Primitives},
  journal = {arXiv preprint arXiv:2412.02998},
  year    = {2024}
}