An efficient laser SLAM point cloud feature extraction algorithm

Wei Xing Chen; Xiao Dong Liang

doi:10.1117/12.2678834

25 May 2023 An efficient laser SLAM point cloud feature extraction algorithm

Wei Xing Chen, Xiao Dong Liang

Proceedings Volume 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023); 127120M (2023) https://doi.org/10.1117/12.2678834
Event: International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 2023, Huzhou, China

Abstract

In this paper, a new point cloud feature extraction algorithm is proposed to address the problems of fixed point cloud feature extraction method, large number of point clouds and low computational efficiency of the current open source laser SLAM. The algorithm follows the original LOAM of calculating point cloud curvature and the screening mechanism of anomalous points, on which a new mechanism of selecting edge feature points and plane feature points is designed to reduce the number of point clouds transmitted to the back end; Finally, this paper transplants it in the open source LIOSAM scheme and completes the experimental validation of the algorithm in different datasets. The evaluation results show that the localization accuracy of this paper's algorithm is better than that of the LIO-SAM algorithm under the same conditions, and it also reduces the computing resources required.

Citation Download Citation

Wei Xing Chen and Xiao Dong Liang "An efficient laser SLAM point cloud feature extraction algorithm", Proc. SPIE 12712, International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2023), 127120M (25 May 2023); https://doi.org/10.1117/12.2678834

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KEYWORDS

Point clouds

Feature extraction

LIDAR

Error analysis

Sensors

Pose estimation

Laser processing

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