基于车载LiDAR数据的道路边界精细提取  

Refining extraction of road boundary from vehicle LiDAR date

作　　者：李永强[1] 王文越[1] 郑艳慧[1] 曹鸿[1] 孙鹏[1] 杨莎莎[1] 

机构地区：[1]河南理工大学测绘与国土信息工程学院,河南焦作454000

出　　处：《河南理工大学学报（自然科学版）》2014年第4期458-462,共5页Journal of Henan Polytechnic University（Natural Science）

基　　金：国家自然科学基金资助项目(41001304);科技部973课题(2011CB707102);国家十二五科技支撑计划项目(2012BAH34B);河南理工大学博士基金资助项目(B2009-33)

摘　　要：根据道路在车载激光点云数据中的表达特征,提出一种基于轨迹线辅助下的K均值聚类算法,开展针对道路边界线的自动精细提取研究,算法描述为:先进行数据预处理,将复杂轨迹简化成单一轨迹;再利用轨迹辅助,通过插入截面,将点云投影在截面上获得"断面线";然后以断面线为基础,采用K均值聚类算法提取出道路边界;最后对提取的道路边界进行检核、优化,获取精细道路边界信息.实验表明,该方法实现了道路边界高效准确地全自动提取.According to the characteristics of the road in vehicle-borne LiDAR point cloud data,analgorithmofk-means clustering was proposed based on the trajectory,aiming to research the automatic extraction method on road boundary.Thealgorithm is described as followings.firstly,simplify the complex trajectory into one single trackfordata preprocessing; then,insert sections using the trajectory,getting section lines through projection in cross section of the point cloud ; thirdly,use K-means clustering algorithm to extract the road boundary based on the section line ; lastly,check and optimize the result for accurate road boundaryinformation.The test resultshows that the algorithmcan automatically extract road boundary efficiently and accurately.

关 键 词：车载 LIDAR 轨迹辅助点云 边界提取 K 均值聚类算法 

分 类 号：P237[天文地球—摄影测量与遥感]