Road Surface Modeling and Representation from Point Cloud Based on Fuzzy Clustering  

作　　者：ZHANG Yi YAN Li 

机构地区：[1]School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China

出　　处：《Geo-Spatial Information Science》2007年第4期276-281,共6页地球空间信息科学学报（英文）

基　　金：Supported by the National Natural Science Foundation of China （No.40471089） and the Key Laboratory of Geo-informatics of State Bureau of Surveying and Mapping.

摘　　要：A scheme for an automatic road surface modeling from a noisy point cloud is presented. The normal vectors of the point cloud are estimated by distance-weighted fitting of local plane. Then, an automatic recognition of the road surface from noise is performed based on the fuzzy clustering of normal vectors, with which the mean value is calculated and the projecting plane of point cloud is created to obtain the geometric model accordingly. Based on fuzzy clustering of the intensity attributed to each point, different objects on the road surface are assigned different colors for representing abundant appearances. This unsupervised method is demonstrated in the experiment and shows great effectiveness in reconstructing and rendering better road surface.为从吵闹的点云当模特儿的自动道路表面的一个计划被介绍。点云的正常向量被本地飞机的距离加权的适合估计。然后，从噪音的道路表面的自动识别基于正常向量，平均值与是计算的模糊聚类被执行，点云的投射飞机被创造因此获得几何模型。基于归因各指的紧张的模糊聚类，道路表面上的不同目标为代表丰富的外观被分配不同颜色。这个无指导的方法在重建并且显示更好的道路表面在实验和表演伟人有效性被表明。

关 键 词：surface modeling point cloud distance-weighted fitting fuzzy clustering normal vectors INTENSITY 

分 类 号：P237.4[天文地球—摄影测量与遥感] TP753[天文地球—测绘科学与技术]