LIDAR pedestrian segmentation based on improved Euclidean clustering

Yiwei Wu; Hongchang Ding

doi:10.1117/12.2686675

10 August 2023 LIDAR pedestrian segmentation based on improved Euclidean clustering

Yiwei Wu, Hongchang Ding

Author Affiliations +

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 127593A (2023) https://doi.org/10.1117/12.2686675
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Target segmentation is an important aspect in autonomous driving systems. In this paper, we propose an improved algorithm based on the Euclidean clustering algorithm for the segmentation of LiDAR pedestrian point clouds, which often results in under-segmentation due to the close distance. After the point cloud pre-processing, the target segmentation is performed based on the point cloud density and spacing as the clustering basis. The experimental results show that the improved algorithm improves the target segmentation accuracy by about 4% and the pedestrian target segmentation accuracy by about 6.4% for the traditional Euclidean clustering algorithm.

Citation Download Citation

Yiwei Wu and Hongchang Ding "LIDAR pedestrian segmentation based on improved Euclidean clustering", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 127593A (10 August 2023); https://doi.org/10.1117/12.2686675

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