A Point-to-distribution Degeneracy Detection Factor for LiDAR SLAM using Local Geometric Models

Sehua Ji1,2; Weinan Chen1; Zerong Su3; Yisheng Guan1; Jiehao Li4; Hong Zhang5; Haifei Zhu1

10.1109/ICRA57147.2024.10610340

2024-05-17

979-8-3503-8458-1

2024 IEEE International Conference on Robotics and Automation (ICRA)

13-17 May 2024

Yokohama, Japan

Limited by the working principles, LiDAR-SLAM systems suffer from the degeneration phenomenon in environments such as long corridors and tunnels, due to the lack of sufficient geometric features for frame-to-frame matching. The accuracy and sensitivity of existing degeneracy detection methods need to be further improved. In this paper, we propose a novel method for degeneracy detection using local geometric models based on point-to-distribution matching. To obtain an accurate description of local geometric models, an adaptive adjustment of voxel segmentation according to the point cloud distribution and density is designed. The codes of the proposed method is open-source and available at https://github.com/jisehua/Degenerate-Detection.git. Experiments with public datasets and self-build robots were conducted to evaluate the methods. The results exhibit that our proposed method achieves higher accuracy than the other existing approaches. Applying our proposed method is beneficial for improving the robustness of the LiDAR-SLAM systems.

其他

EI

1.Biomimetic and Intelligent Robotics Lab (BIRL), School of Electromechanical Engineer, Guangdong University of Technology, Guangzhou, China
2.JT-Innovation (Guangdong) Intelligent Technology Co., Ltd.
3.Guangdong Key Laboratory of Modern Control Technology, Institute of Intelligent Manufacturing, Guangdong Academy of Sciences, Guangzhou, China
4.College of Engineering, South China Agricultural University, China
5.Shenzhen Key Laboratory of Robotics and Computer Vision, Southern University of Science and Technology, China

Sehua Ji,Weinan Chen,Zerong Su,et al. A Point-to-distribution Degeneracy Detection Factor for LiDAR SLAM using Local Geometric Models[C],2024.