基于点云空间索引的隧道断面提取方法  

A Tunnel Section Extraction Method Based on Point Cloud Spatial Index

作　　者：朱林川 黄声享[1] 张赛 王新鹏 ZHU Linchuan;HUANG Shengxiang;ZHANG Sai;WANG Xinpeng(School of Geodesy and Geomatics,Wuhan University,Wuhan 430079,China;Troops 96881,Luoyang 471031,China;Mining College of Guizhou University,Guiyang 550225,China;Beijing Key Laboratory of Urban Spatial Information Engineering,Beijing 100038,China)

机构地区：[1]武汉大学测绘学院,湖北武汉430079 [2]96881部队,河南洛阳471031 [3]贵州大学矿业学院,贵州贵阳550225 [4]城市空间信息工程北京市重点实验室,北京100038

出　　处：《测绘地理信息》2023年第3期45-49,共5页Journal of Geomatics

基　　金：城市空间信息工程北京市重点实验室研究基金(2019210)。

摘　　要：探索了一种基于三维激光点云空间索引的隧道断面提取方法。该方法主要通过建立空间索引,利用k维树(kdimensional tree,KD树)和规则格网提取频数分布特征,采用含有多种几何模型约束条件的随机抽样一致(random sample consensus,RANSAC)算法完成断面拟合。实验结果表明,该方法对隧道点云的平均降噪率达到97%,能准确提取隧道断面。A tunnel section extraction method based on 3D la⁃ser point cloud spatial index is explored.In this method,spa⁃tial index is established,frequency distribution features are ex⁃tracted by k-dimensional tree and regular grid,and section fit⁃ting is completed by random sample consensus(RANSAC)algorithm with various geometric model constraints.The ex⁃perimental results show that the average noise reduction rate of tunnel point cloud reaches 97%and the tunnel section can be accurately extracted by this method.

关 键 词：隧道 点云 空间索引 k维树 随机抽样一致(random sample consensus RANSAC) 断面 

分 类 号：P237[天文地球—摄影测量与遥感] P258[天文地球—测绘科学与技术]