A novel statistical method for 3D range data registration based on Lie group framework

摘要

Registration of 3D range data is to find the transformation that best maps one data set to the other. In this paper, Lie group parametric representation is combined with the Expectation Maximization (EM) method to provide a unified framework. First, having a transformation fixed, the EM algorithm is introduced to find the correspondence between two data sets through correspondence probability, which covers the relationship of all points, instead of using exact correspondence such as the classical Iterative Closest Point (ICP) method. With this type of ststistical correspondence, we could deal with the presence of the degradations such as outliers and incomplete point sets. Second, having the updated correspondence fixed, and introducing Lie group parametric representation, the transformation is updated by minimizing a quadratic programming. Then, an alternative iterative strategy by the above two steps is used to approximate the desired correspondence and transformation. The comparative experiment between our Lie-EM-ICP algorithm and Lie-ICP algorithm using point cloud is presented. Our algorithm is demonstrated to be accurate and robust, especially in the presence of incomplete point sets and outliers.