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利用RANSAC算法对建筑物立面进行点云分割
引用本文:李娜,马一薇,杨洋,高晟丽.利用RANSAC算法对建筑物立面进行点云分割[J].测绘科学,2011,36(5):144-145,138.
作者姓名:李娜  马一薇  杨洋  高晟丽
作者单位:1. 中国科学院遥感应用研究所,北京100101;96633部队,北京100096
2. 61512部队,北京100088;
3. 信息工程大学测绘学院,郑州,450052
摘    要:建筑物立面点云分割是车载激光扫描数据特征提取与建模的基础.本文将随机抽样一致性算法( Random Sampling Consensus)方法引入对点云的分割中,并在判断准则中引入了点云的r半径密度,消除了噪声的影响,同时建立角度和距离两个约束条件对平面分割结果进行优化,提取出了最终的建筑物立面特征平面.

关 键 词:车载激光扫描  随机抽样一致性  点云分割  r半径密度

Segmentation of building facade point clouds using RANSAC
LI Na,MA Yi-wei,YANG Yang,GAO Sheng-li.Segmentation of building facade point clouds using RANSAC[J].Science of Surveying and Mapping,2011,36(5):144-145,138.
Authors:LI Na  MA Yi-wei  YANG Yang  GAO Sheng-li
Institution:③(①Institute of Remote Sensing Applications,Chinese Academy of Sciences,Beijing 100101,China;②Troops 96633,Beijing 100096,China;③Troops 61512,Beijing 100088,China;④Institute of Surveying and Mapping,Information Engineering University,Zhengzhou 450052,China)
Abstract:Segmentation of building facade point clouds is the foundation of feature extraction and modeling from Vehicle-Borne LiDAR.In the paper,Random Sampling Consensus was introduced into the segmentation of LiDAR and r-radius point density was put forward to the estimation criterion,which aims to remove the discrete point outside the feature plane.Then two constraints of angle and distance were erected to unite the segmented planes which optimized the results.
Keywords:vehicle-borne LiDAR  RANSAC  segmentation of point clouds  r-radius point density  
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