一种基于最小广义方差估计的TLS点云抗差法向量求解方法  

A Robust Normal Estimation Method for Terrestrial Laser Scanning Point Cloud Based on Minimum Covariance Determinant

作　　者：冯林 李斌兵[1] FENG Lin;LI Binbing(Department of Information Engineering,Engineering University of People＇s Armed Police,Xi＇an 710086,China)

机构地区：[1]武警工程大学信息工程系

出　　处：《武汉大学学报（信息科学版）》2018年第11期1647-1653,共7页Geomatics and Information Science of Wuhan University

基　　金：国家自然科学基金(41171224)~~

摘　　要：针对地面激光扫描点云中的粗差与不均匀采样对法向量计算的影响,基于最小广义方差估计与局部平面拟合原理提出了一种抗差法向量求解方法。首先通过快速近似最近邻居搜索算法得到最近k邻居点集,然后由确定型最小广义方差估计方法和多元马氏距离得到邻居点集协方差矩阵的抗差估计,最后根据主成分分析法（principal component analysis,PCA）计算得到抗差法向量。通过构造的模拟地面激光扫描（terrestrial laser scanning,TLS）点云数据将提出的方法分别与基于PCA、鲁棒PCA和随机抽样一致的法向量求解方法进行实验比较。结果表明,所提方法的抗差性能优异,且并行优化改进后可以满足大规模TLS点云的计算需求。将该方法应用于实际野外地形TLS点云数据,由求解的抗差法向量重建的泊松表面更符合实际地形,表明了该方法在实际应用中的有效性。A robust normal estimation method based on local plane fitting and minimum covariance determinant（MCD）is proposed for terrestrial laser scanning（TLS）point cloud with gross errors and non-uniform sampling.Firstly,fast library for approximate nearest neighbors algorithm is performed to retrieve k nearest neighbor point set.Then,robust estimation of its covariance is calculated by DetMCD（deterministic MCD）and multivariate Mahalanobis distance.Finally,robust estimation of normal vector is calculated through principal component analysis（PCA）method.Compared with PCA,robust PCA and random sample consensus based normal estimation method,on simulated TLS point cloud,experimental results show that the proposed method can get more accurate normal estimation under the influences of gross errors.And its parallel improvement can meet the requirement of efficiency for large scale TLS point cloud processing.Further experiment on real TLS data from natural terrain shows that the proposed method helps to better Poisson surface reconstruction and prove its effectiveness in practical application.

关 键 词：点云 地面激光扫描 法向量求解 主成分分析 确定型最小广义方差估计 

分 类 号：P237[天文地球—摄影测量与遥感]