同济大学学报（自然科学版）2011，Vol.39Issue(9)：1350-1354,5.DOI:10.3969/j.issn.0253-374x.2011.09.018
改进的鲁棒迭代最小二乘平面拟合算法
An Improved Robust Method for Iterating Least-Squares Plane Fitting
摘要
Abstract
The iterating eigenvalue least-squares is not robust,so an improved statistic analysis method is introduced for fitting a plane to point clouds containing a great amount of outliers. The method is robust iterating least-squares (RILS). Firstly, some plane models are fitted to the local neighborhoods around sample points by moving least-squares (MLS),and then a good model is selected from those models by least median of squares (LMS)and refined to appropriate the whole point set through eliminating those outliers by iterating eigenvalue least-squares. Different from other backward ways, this method is robust, which retains the accuracy of the original method, and furthermore accelerates the convergence of iteration.关键词
平面拟合/最小平方中位数法/移动最小二乘法/迭代特征值最小二乘法Key words
plane fitting/least median of squares/moving least-squares/iterating eigenvalue least-squares分类
天文与地球科学引用本文复制引用
王峰,丘广新,程效军..改进的鲁棒迭代最小二乘平面拟合算法[J].同济大学学报（自然科学版）,2011,39(9):1350-1354,5.基金项目
国家自然科学基金(40971241) （40971241）
广州市科技计划项目(11G0041) （11G0041）
