基于Alpha Shapes轮廓点云识别算法的洞室表面形变区域提取方法  

Extraction of cavern surface deformation regions based on Alpha Shapes contour point cloud recognition algorithm

作　　者：张雨婷 郑德华[1] 李思远 ZHANG Yuting;ZHENG Dehua;LI Siyuan(School of Earth Sciences and Engineering,Hohai University,Nanjing 211100,China)

机构地区：[1]河海大学地球科学与工程学院,南京211100

出　　处：《南京信息工程大学学报》2025年第2期181-190,共10页Journal of Nanjing University of Information Science & Technology

基　　金：钱投科创项目(QT202208A001)。

摘　　要：针对三维激光扫描密集点云提取洞室表面变形信息的问题,本文提出一种基于改进的Alpha Shapes算法识别洞室轮廓点云和多尺度模型到模型的点云比对(Multiscale Model-to-Model Cloud Comparison,M3C2)的洞室表面变形监测方法.首先对获取到的两期洞室表面点云数据进行配准,采用改进的Alpha Shapes算法识别洞室表面外轮廓点云.获得的两期洞室表面外轮廓点云经精配准后,再采用M3C2算法进行各点变形值计算,最后进行距离聚类提取连续形变区域.实验结果表明:该方法能够有效剔除点云中细小沟壑处的点及受到混合像元影响的点,在洞室截面到扫描仪距离10 m的范围内,两期点云剔除率分别为14.17%及13.52%,在70 m范围内,分别为6.25%及6.42%;该方法能够准确高效地提取出2倍配准误差以上的洞室表面形变区域.Aiming at the extraction of cavern surface deformation from three-dimensional laser scanning dense point clouds,we propose a method integrating the Multiscale Model-to-Model Cloud Comparison(M3C2)with an improved Alpha Shapes algorithm.First,the two-phase surface point cloud data are registered,and the improved Alpha Shapes algorithm is used to identify the outer contour point clouds.After the fine registration of these two-phase outer contour point clouds,the M3C2 algorithm calculates the deformation value of each point,and finally the continuous deformation regions are extracted through distance clustering.Experimental results show that the proposed method effectively eliminates the points at small furrows as well as those affected by mixed pixels.Specifically,the removal rates of point clouds in the two phases within 10 m from the scanner to the cavern section are 14.17%and 13.52%,respectively,which are 6.25%and 6.42%within 70 m.This method accurately and efficiently extracts the cavern surface deformation regions with more than twice the registration error.

关 键 词：洞室变形监测 轮廓点云识别 Alpha Shapes算法 M3C2算法 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]