Robust Generalized Point Cloud Registration Using Hybrid Mixture Model

Abstract

This paper introduces a robust point cloud registration method which utilizes not only positional but also the orientation information at each point. The proposed method takes a probabilistic approach which forms the problem as a hybrid mixture model, in which a Von-Mises-Fisher mixture model (FMM) is adopted to model the orientation part and a gaussian mixture model (GMM) is used to represent the position part. When two point clouds are optimally registered, the correspondence is the maximum of the posterior probability of the overall mixture model. Expectation-Maximization (EM) algorithm has been adopted to solve the optimization problem in an iterative manner to find the optimal rotation and translation between two point clouds. Extensive experiments under different noise levels and different outlier ratios have been carried out on a dataset of the femur CT images. Comparison results show that the proposed method outperforms the state-of-the-art methods under most of the experimental conditions, which indicates the validity of our method.