A robust method for registration and segmentation of multiple range images

Abstract

Registration and segmentation of multiple range images are one of the most important problems in range image analysis. This problem has been investigated by a number of researchers, but most of existing methods are easily affected by outlying points (outliers) like noise and occlusion. We first propose a robust method of estimating rigid motion parameters from a pair of range images. This method is an integration of the iterative closest point (ICP) algorithm with the random sampling and the least median of squares (LMS) estimator. We then detect the outliers by thresholding the residuals in the LMS estimation, and finally we classify each pixel into one of five categories to obtain a segmentation. We experimented on real range images taken by two kinds of rangefinders, and observed that our method worked successfully even for noisy data. The proposed method has another advantage of reducing the computational cost. >