MDPE: A Very Robust Estimator for Model Fitting and Range Image Segmentation

摘要

In this paper, we propose a novel and highly robust estimator, called MDPE1 (Maximum Density Power Estimator). This estimator applies nonparametric density estimation and density gradient estimation techniques in parametric estimation (“model fitting”). MDPE optimizes an objective function that measures more than just the size of the residuals. Both the density distribution of data points in residual space and the size of the residual corresponding to the local maximum of the density distribution, are considered as important characteristics in our objective function. MDPE can tolerate more than 85% outliers. Compared with several other recently proposed similar estimators, MDPE has a higher robustness to outliers and less error variance.