Point Cloud Fitting Method Using the Nonlinear Gauss-Helmert Model and Robust Estimation

被引：0

作者：

Zhao, Zhisheng ^{[1
]}

Chen, Yu ^{[1
]}

Wang, Bin ^{[1
]}

机构：

[1] School of Geomatics Science and Technology, Nanjing Tech University, Nanjing,211816, China

来源：

Wuhan Daxue Xuebao (Xinxi Kexue Ban)/Geomatics and Information Science of Wuhan University | 2024年 / 49卷 / 07期

关键词：

Objectives:; Currently; most of the existing point cloud fitting methods are based on linear Gauss-Markov (GM) or errors-in-variables (EIV) models; which cannot be strictly applied to nonlinear surface fitting problems. The mathematical model of point cloud fitting is abstracted as a more general nonlinear Gauss-Helmert (GH) model. To deal with the case when there exist outliers in the dataset; we further introduce an equivalent weight scheme and propose a point cloud fitting method based on robust nonlinear Gauss-Helmert (RGH) model. Methods: In this method; the covariance matrices of point coordinates derived from the errors of the original observations are treated as the prior random model; and the weight function is constructed using standardized residuals and median to carry out the robust iterative calculation. Results: Under the conditions with outliers; the root mean square errors of parameters for the simulated sphere data obtained by RGH model are only 25.77%-30.67% of those from random sample consensus (RANSAC) method; and the standard deviations of parameters for the real data are only 4.63%-5.49% of those from RANSAC method; respectively. Conclusions: The experimental results demonstrate the significant advantages of the proposed method in terms of the accuracy and robustness of point cloud fitting. © 2024 Editorial Department of Geomatics and Information Science of Wuhan University. All rights reserved;

D O I：

10.13203/j.whugis20220072

中图分类号：

学科分类号：

摘要：

引用

页码：1201 / 1211

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