文献类型：期刊文献

中文题名：一种基于3D激光雷达的实时道路边缘提取算法

英文题名：Real-time Road Edge Extraction Algorithm Based on 3D-Lidar

作者：李广敬[1];鲍泓[1];徐成[1,2]

第一作者：李广敬

机构：[1]北京联合大学北京市信息服务工程重点实验室;[2]北京邮电大学信息网络中心

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2018

卷号：45

期号：9

起止页码：294-298

中文期刊名：计算机科学

外文期刊名：Computer Science

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

基金：国家自然科学基金重大研究计划项目:智能车驾驶脑认知技术;平台与转化研究(91420202);Newton Fund Project:Talents Cultivation and Cooperation Oriented to Intelligent Vehicle Industrialization(UK-CIAPP\324)资助

语种：中文

中文关键词：无人驾驶车;道路边缘提取;3D激光雷达;随机抽样一致性算法;卡尔曼滤波

外文关键词：Driverless car;Road edge extraction;3D-Lidar;Random sample consensus;Kalman filter

摘要：无人驾驶车辆在道路中行驶时需要判定当前环境中的可行驶区域,针对这一问题,提出一种基于3D激光雷达的道路边缘实时提取算法。该算法首先在栅格化和分层处理后的激光雷达点云图中分别提取高度特征和平滑特征,以进一步通过道路宽度约束筛选得到候选边缘点,然后利用随机抽样一致性算法(RANSAC)对两侧路沿点进行多项式拟合,最后通过卡尔曼滤波对边缘点进行预测、跟踪。实验结果表明,该算法在园区场景和城市开放道路上都能实时、稳定地提取道路边缘,且此算法在"2017年世界智能驾驶挑战赛"中得到了成功应用。
A real-time road edge extraction algorithm based on 3D-lidar was put forward for the environmental perception of driverless cars.In this algorithm,the height feature points and the smooth feature points are extracted separately in the maps rasterized and layered from the lidar points cloud followed by the constraint of the road width to obtain the candidate edge points.Then the candidate points are polynomial fitted by the algorithm of random sample consensus(RANSAC).Finally,Kalman filter is used to predict and track the road edge.The experimental results show that the proposed algorithm can extract the edge of road in real time and robustly in both park and urban roads.What’s more,this algorithm has been applied successfully in 2017 World Intelligent Driving Challenge.