摘要
针对三维激光扫描技术在获取巷道内壁点云数据时会包含大量非巷道内壁点,无法快速有效地获取巷道围岩形变信息的问题。该文提出一种基于局部最优邻域法向量估算的巷道点云去噪方法,该方法采用自适应邻域半径的主成分分析算法,提高了点云法向量估算的精度和方向一致性,较好地解决了区域生长算法提取巷道内壁点云时存在的孔洞过多与噪声点云去除不彻底的问题,实现了巷道内壁点云较为完整的获取。通过不同类型的巷道点云数据进行验证,结果表明,该方法能够有效地去除非巷道内壁点云,提高巷道内壁点云获取的精度。
Aiming at the problem that 3D laser scanning technology contains a large number of non-inner wall points when acquiring the point cloud data of the inner wall of the roadway,it was impossible to obtain the deformation information of surrounding rock quickly and effectively.This paper proposed a method of roadway point cloud denoising based on local optimal neighborhood normal vector estimation.In this method,the principal component analysis algorithm with adaptive neighborhood radius was used to improve the accuracy and direction consistency of point cloud normal vector estimation.It solved the problems of excessive holes and incomplete noise point cloud removal when a region growing algorithm was used for extracting the point cloud of the inner wall of the roadway,and realized the complete acquisition of point cloud on the inner wall of roadway.Verified by different types of roadway point cloud data,the experimental results demonstrated that this method could effectively remove the non-roadway inner wall points and improved the accuracy of the roadway inner wall point cloud acquisition.
作者
郑理科
王健
李志远
梁晓鹏
ZHENG Like;WANG Jian;LI Zhiyuan;LIANG Xiaopeng(College of Geodesy and Geomatics,Shandong University of Science and Technology,Qingdao,Shandong 266590,China;Jiaojia Gold Mine,Shandong Gold(Laizhou)Co.,Ltd.,Yantai,Shandong 261441,China)
出处
《测绘科学》
CSCD
北大核心
2023年第4期140-148,171,共10页
Science of Surveying and Mapping
基金
高端外国专家引进计划项目(G2021025006L)。
关键词
巷道
局部最优邻域
主成分分析
区域生长
点云去噪
roadway
local optimal neighborhood
principal component analysis
region growth
point cloud denoising
