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

被引：0
|
作者：
Zong C.-F. [1 ]
Wen L. [1 ]
He L. [1 ]
机构：
[1] State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2020年 / 50卷 / 01期
关键词：
Automatic driving; Engineering of communication and transportation system; Euclidean cluster; Lidar; Object detection;
D O I：
10.13229/j.cnki.jdxbgxb20181242
中图分类号：
学科分类号：
摘要：
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. © 2020, Jilin University Press. All right reserved.
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收藏
页码：107 / 113
页数：6
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