Ellipsoidal neighborhood clustering algorithm for vehicular LIDAR obstacle detection

Lin Li; Guoqing Zhou; Ruixiang Li; Yangleijing Li; Shuaiguang Zhu

doi:10.1117/12.3041258

11 September 2024 Ellipsoidal neighborhood clustering algorithm for vehicular LIDAR obstacle detection

Lin Li, Guoqing Zhou, Ruixiang Li, Yangleijing Li, Shuaiguang Zhu

Proceedings Volume 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024); 132531L (2024) https://doi.org/10.1117/12.3041258
Event: 4th International Conference on Signal Image Processing and Communication, 2024, Xi'an, China

Abstract

In the obstacle detection process of lidar-based intelligent vehicles, there exists the phenomenon of inaccurate clustering of obstacle point clouds, which leads to the problem of misdetection and omission of obstacle detection. For this reason, this paper proposes an ellipsoidal neighborhood obstacle point cloud clustering algorithm (ENC), which firstly analyzes the spatial distribution of the vehicle lidar point cloud, then selects the sampling points and compares the distances between them and the surrounding points, then determines the long and short axes of the ellipsoid, and finally performs the clustering. The KITTI point cloud set is used to validate this paper's algorithm and compare the results with DBSCAN algorithm and Euclidean clustering algorithm. The experiments show that the ENC algorithm in this paper has the highest positive detection rate of 95.82% and the time consumed meets the real-time requirements of detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Lin Li, Guoqing Zhou, Ruixiang Li, Yangleijing Li, and Shuaiguang Zhu "Ellipsoidal neighborhood clustering algorithm for vehicular LIDAR obstacle detection", Proc. SPIE 13253, Fourth International Conference on Signal Image Processing and Communication (ICSIPC 2024), 132531L (11 September 2024); https://doi.org/10.1117/12.3041258

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