基于欧几里得聚类算法的三维激光雷达障碍物检测技术

doi:10.13229/j.cnki.jdxbgxb20181242

Abstract

Abstract:

Automatic driving vehicles need lidar to detect the object while driving. Because of the moving of vehicles, the point cloud becomes inaccurate while the traditional Euclidean clustering algorithm can not detect the obstacles in both near and remote areas at the same time, which leads to an inaccurate result of the number of those obstacles. In order to solve this problem, a algorithm is proposed to correct the 3D lidar point cloud, which is inaccurate, meanwhile, improve the Euclidean cluster algorithm, so it be able to change the distance limit according to the distance between the obstacle and lidar. Experimental results illustrate that the proposed algorithm can detect the obstacle in both near and remote areas, and the detect distance is increased compared with the traditional method.

Key words: engineering of communication and transportation system, automatic driving, Euclidean cluster, object detection, lidar

CLC Number:

Chang-fu ZONG,Long WEN,Lei HE. Object detection based on Euclidean clustering algorithm with 3D laser scanner[J].Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 107-113.

Figures/Tables 9

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Object detection based on Euclidean clustering algorithm with 3D laser scanner

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