Real-time Lidar SLAM Algorithm Based on Distribution Optimal Registration

Authors

Weigang Li, Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China; School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Chuxiang Yu, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Yongqiang Wang, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Shaofeng Zou, School of Information Science and Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Abstract

Abstract: When scanning the surrounding environment, a lidar will generate some cluttered and sparse point cloud, which will cause excessive distribution fitting errors and correlation distances in the registration process, thus affecting the accuracy of the registration algorithm and the effect of simultaneous localization and mapping (SLAM). To address this problem, a real-time lidar SLAM algorithm based on distribution optimal registration is proposed. An eigenspectrum filter is designed, which takes the normalized minimum eigenvalue as the filtering object to filter out the points that do not match the set distribution in order to reduce the distribution fitting error. Secondly, a point cloud registration loss function is proposed for the compound normalization of the joint covariance matrix and error terms composed of the source and target point clouds to reduce the interference caused by points with excessive correlation distance in the iterative solution process. A SLAM algorithm framework is designed which contains front-end odometry, loop-closure detection, and back-end optimization. It is compatible with pure lidar mapping and lidar/inertial fusion mapping, thus ensuring the accuracy and consistency of the mapping and improving the adaptability of the algorithm. Several sets of experiments are conducted on public datasets. The experimental results show that the proposed algorithm has great advantages in terms of accuracy and speed compared with the existing state-of-the-art SLAM algorithms.

Recommended Citation

Li, Weigang; Yu, Chuxiang; Wang, Yongqiang; and Zou, Shaofeng (2024) "Real-time Lidar SLAM Algorithm Based on Distribution Optimal Registration," Journal of System Simulation: Vol. 36: Iss. 11, Article 6.
DOI: 10.16182/j.issn1004731x.joss.23-0887
Available at: https://dc-china-simulation.researchcommons.org/journal/vol36/iss11/6

First Page

2566

Last Page

2577

CLC

TP242.6

Recommended Citation

Li Weigang, Yu Chuxiang, Wang Yongqiang, et al. Real-time Lidar SLAM Algorithm Based on Distribution Optimal Registration[J]. Journal of System Simulation, 2024, 36(11): 2566-2577.

DOI

10.16182/j.issn1004731x.joss.23-0887