基于三维激光雷达的道路边界提取和障碍物检测算法

doi:10.16451/j.cnki.issn1003-6059.202004008

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摘要为了快速有效地提取智能车辆在不同环境下的道路环境信息,提出基于三维激光雷达的道路边界提取和障碍物检测算法.首先,对三维激光雷达点云数据进行栅格化滤波处理,利用单束激光点云空间邻域联合分割的方法进行空间分析,得到点云平滑度特征图像.然后,采用自适应方向搜索算法获取道路边界候选点,并进行聚类分析和曲线拟合.最后,对道路边界约束下可通行区域内点云进行聚类分割,获得道路内障碍物方位和距离信息.实验表明,文中算法能够实时准确地提取道路边界和障碍物位置信息,满足智能车环境建模和路径规划的需求.

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	王灿
	孔斌
	杨静
	王智灵
	祝辉

关键词 ：无人驾驶汽车, 三维激光雷达, 道路边界, 障碍物检测, 点云处理

Abstract：To extract relevant road information quickly and effectively for intelligent vehicles in various road environments, an algorithm for real-time road boundary extraction and obstacle detection based on three-dimensional (3D) lidar is proposed. Firstly, 3D lidar point cloud data is rasterized and filtered, and the single beam laser point cloud spatial segmentation method is employed for spatial analysis to obtain the point cloud smoothness characteristic image. Then, the adaptive direction search algorithm is adopted to obtain the road boundary feature points and perform cluster analysis and curve fitting. Finally, the point cloud in the passable area is clustered and segmented under the road boundary constraint to obtain the obstacle position information. Experiments show that the proposed algorithm extracts road boundary and obstacle location information accurately in real time, and it meets the requirements of environment modeling and path planning for intelligent vehicle.

Key words： Unmanned Vehicle 3D Lidar Road Boundary Obstacle Detection Point Cloud Processing

收稿日期: 2019-10-26

ZTFLH:

TP 391.4

基金资助:国家自然科学基金重大研究计划集成项目(No.913203002)、安徽省新能源汽车暨智能网联汽车产业技术创新工程项目、中国科学院机器人与智能制造创新研究院项目(No.CO2018

通讯作者: 王灿,硕士,助理研究员,主要研究方向为模式识别、计算机视觉.E-mail:cwang@iim.ac.cn.

作者简介: 孔斌,博士,研究员,主要研究方向为智能机器人、机器视觉.E-mail:bkong@iim.ac.cn.杨静,博士,副研究员,主要研究方向为模式识别、计算机视觉.E-mail:jyang@iim.ac.cn.王智灵,博士,研究员,主要研究方向为无人驾驶、机器视觉.E-mail:zlwang@hfcas.ac.cn.祝辉,博士,副研究员,主要研究方向智能机器人、多传感器信息融合.E-mail:hzhu@iim.ac.cn.

引用本文:

王灿, 孔斌, 杨静, 王智灵, 祝辉. 基于三维激光雷达的道路边界提取和障碍物检测算法[J]. 模式识别与人工智能, 2020, 33(4): 353-362. WANG Can, KONG Bin, YANG Jing, WANG Zhiling, ZHU Hui. An Algorithm for Road Boundary Extraction and Obstacle Detection Based on 3D Lidar. , 2020, 33(4): 353-362.

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http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.202004008 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2020/V33/I4/353

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