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.
出处
《河南理工大学学报（自然科学版）》
CAS
北大核心
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)
关键词
车载
LIDAR
轨迹辅助点云
边界提取
K
均值聚类算法
vehicle-borne LiDAR
trajectory auxiliary
point cloud
refining extraction
K-meanselustering
