A Fast Spatial Clustering Method for Sparse LiDAR Point Clouds Using GPU Programming

Author:

Tian Yifei,Song WeiORCID,Chen LongORCID,Sung YunsickORCID,Kwak JeonghoonORCID,Sun Su

Abstract

Fast and accurate obstacle detection is essential for accurate perception of mobile vehicles’ environment. Because point clouds sensed by light detection and ranging (LiDAR) sensors are sparse and unstructured, traditional obstacle clustering on raw point clouds are inaccurate and time consuming. Thus, to achieve fast obstacle clustering in an unknown terrain, this paper proposes an elevation-reference connected component labeling (ER-CCL) algorithm using graphic processing unit (GPU) programing. LiDAR points are first projected onto a rasterized x–z plane so that sparse points are mapped into a series of regularly arranged small cells. Based on the height distribution of the LiDAR point, the ground cells are filtered out and a flag map is generated. Next, the ER-CCL algorithm is implemented on the label map generated from the flag map to mark individual clusters with unique labels. Finally, obstacle labeling results are inverse transformed from the x–z plane to 3D points to provide clustering results. For real-time 3D point cloud clustering, ER-CCL is accelerated by running it in parallel with the aid of GPU programming technology.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 6 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. 3D closed-loop surface-related multiple elimination based on GPU acceleration;Journal of Applied Geophysics;2024-09

2. Neural network-based 3D point cloud detection of targets in unstructured environments;Advances in Mechanical Engineering;2024-07

3. Towards an obstacle detection system for robot obstacle negotiation;Industrial Robot: the international journal of robotics research and application;2024-02-06

4. Local Metric Dimension of Certain Classes of Circulant Networks;Journal of Advanced Computational Intelligence and Intelligent Informatics;2023-07-20

5. A 3D Object Recognition Method From LiDAR Point Cloud Based on USAE-BLS;IEEE Transactions on Intelligent Transportation Systems;2022-09

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