基于机载LiDAR点云数据的改进滤波算法  

Improved Filtering Algorithm Based on Airborne LiDAR Point Cloud Data

作　　者：焦元冰 陆爱萍 JIAO Yuanbing;LU Aiping(Zhejiang Institute of Surveying and Mapping Science and Technology,Hangzhou,Zhejiang 310030,China)

机构地区：[1]浙江省测绘科学技术研究院,浙江杭州310030

出　　处：《测绘标准化》2022年第3期42-46,共5页Standardization of Surveying and Mapping

摘　　要：为了改善经典渐进三角网滤波算法中因地面种子点选取随意造成的机载点云滤波精度受损问题,提出一种改进渐进三角网滤波算法。首先,通过扩展局部极小值法搜索得到格网内极小值点,并将极小值点作为待定地面种子点,弥补经典渐进三角网滤波算法中将格网内高程最小值点作为地面种子点的不足;其次,通过局部薄板样条插值法对格网高程进行拟合计算;最后,通过种子点构建不规则三角网并实现点云分类。对改进滤波算法与经典渐进三角网滤波算法进行点云滤波对比试验,结果表明,改进滤波算法可进一步提高点云滤波的精度。In order to improve the filtering accuracy of airborne point cloud caused by random selection of ground seed points in the classical progressive triangulation filtering algorithm,an improved progressive triangulation filtering algorithm is proposed in this paper.Firstly,the minimum points in the grid are searched by the extended local minimum method,and the minimum points are used as the undetermined ground seed points,which makes up for the deficiency of using the least elevation points in the grid as the ground seed points in the classical progressive triangulation filtering algorithm;Secondly,the grid elevation is fitted and calculated by local thin plate spline interpolation method;Finally,irregular triangulation network is constructed through seed points and point cloud classification is realized.This paper also conducts point cloud filtering experiments on the improved filtering algorithm and the classic progressive triangulation filtering algorithm,the results show that the improved filtering algorithm can further improve the filtering accuracy of point cloud.

关 键 词：机载LIDAR 点云滤波 滤波算法 种子点 薄板样条函数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]