A Robust Method for Registration and Segmentation of Multiple Range Images

Registration and segmentation of multiple range images are important problems in range image analysis. We propose a new algorithm of range data registration and segmentation that is robust in the presence of outlying points (outliers) like noise and occlusion. The registration algorithm determines rigid motion parameters from a pair of range images. Our method is an integration of the iterative closest point (ICP) algorithm with random sampling and least median of squares (LMS or LMedS) estimator. The segmentation method classifies the input data points into four categories comprising inliers and 3 types of outliers. Finally, we integrate the inliers obtained from multiple range images to construct a data set representing an entire object. We have experimented with our method both on synthetic range images and on real range images taken by two kinds of rangefinders. The proposed method does not need preliminary processes such as smoothing or trimming of isolated points because of its robustness. It also offers the advantage of reducing the computational cost.