HU X J, LI S D, TAN J X, et al． An optimization method for laser SLAM point clouds based on pose graph[J]. Journal of Henan Polytechnic University( Natural Science)，doi: 10.16186/j.cnki.1673-9787.2025010030.

doi: 10.16186/j.cnki.1673-9787.2025010030.

Received： 2025-01-22

Revised： 2025-04-27

Online： 2025-12-12

An optimization method for laser SLAM point clouds based on pose graph(Online)

Hu Xiaojian 1, Li Shaoda 1, Tan Junxiang 1, HU Guyue1, Liu Tao1, Yang Xiaojun 2

1.School of Earth and Planetary Sciences, Chengdu University of Technology, Chengdu 610059, Sichuan, China； 2.Southwest Electric Power Design Institute Co.,Ltd. of China Power Engineering Consulting Group，Chengdu 610056, Sichuan, China

Abstract: The Laser scanning system based on Simultaneous Localization and Mapping (SLAM) has been widely used in the field of mapping. It is favored for their low cost and high efficiency. However, numerous studies have shown that SLAM real-time mapping errors accumulate over time during scanning, which leads to severe ghosting effects in revisited point clouds. Objectives This paper aims to propose a SLAM point clouds optimization method based on pose graph to improve the local consistency and overall accuracy of point clouds. Methods The proposed method first designs a point clouds segmentation strategy that jointly considers temporal and spatial dynamics. Then, a loop closure detection algorithm is constructed, which integrates the pose node distance and the global feature similarity of point clouds edge points to find pose nodes with constraints. The constraints between pose nodes are derived through coarse registration based on Normal Distributions Transform(NDT) and fine registration using "point-to-plane" Iterative Closest Point(ICP). Finally, pose graph optimization is performed at both local and global levels, and the corrected trajectory is used to refine the point clouds. Results To validate the effectiveness of the proposed method, experiments were conducted using four sets of vehicle-mounted laser SLAM data. After optimization, the pose estimation accuracy was improved by 81.4%, 93.4%, 81.2%, and 66.6%, respectively. The root mean square error (RMSE) of corresponding point pairs in the four point clouds was reduced from 268.4, 169.3, 138.5, and 89.6 cm to 25.6, 31.2, 15.7, and 11.3 cm, respectively. The experimental results showed that after optimization, the drift phenomenon between different scan frame point clouds of the same object was reduced. The root mean square error between corresponding points decreased by approximately 87%. The internal inconsistency of the point clouds was effectively eliminated. Conclusions This method can address the drift error problem in long-distance scanning and provides significant technical support for high-precision point clouds acquisition.

Key words: simultaneous localization and mapping; graph optimization; point clouds registration; loop closure detection; point clouds correction