摘要
利用三维激光雷达对车辆前方障碍物进行辨识过程中,车辆的运动导致点云数据出现畸变,传统欧氏聚类方法也无法同时对远处和近处的障碍物进行精确检测,从而导致错误的聚类结果,容易出现障碍物误检或漏检的情况。针对上述问题,提出了一种对三维激光雷达点云数据去畸变的方法,同时改进了欧氏聚类方法,使其能自动更正距离阈值,从而使障碍物检测更加快速准确。对本文方法进行了实车试验,试验结果表明:本方法能同时快速准确地检测出较近和较远的障碍物。
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 3 D 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.
作者
宗长富
文龙
何磊
ZONG Chang-fu;WEN Long;HE Lei(State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China)
出处
《吉林大学学报（工学版）》
EI
CAS
CSCD
北大核心
2020年第1期107-113,共7页
Journal of Jilin University:Engineering and Technology Edition
基金
国家自然科学基金项目(51505179)
国家重点研发计划项目(2017YFB0102601).
关键词
交通运输系统工程
智能驾驶
欧氏聚类
障碍物检测
激光雷达
engineering of communication and transportation system
automatic driving
Euclidean cluster
object detection
lidar
作者简介
宗长富(1962-),男,教授,博士生导师.研究方向:汽车动力学仿真与控制,汽车智能驾驶技术.E-mail:zong.changfu@ascl.jlu.edu.cn;通信作者:何磊(1982-),男,副教授,博士.研究方向:汽车智能驾驶技术.E-mail:jlu_helei@126.com
