Probability iterative closest point algorithm for position estimation

This paper proposes probability iterative closest point (ICP) method based on expectation maximization (EM) estimation for point set registration with noise. The classical ICP algorithm can deal with rigid registration between two point sets effectively, but always fails to register point sets with noise. In order to improve the registration precision, a Gaussian model is introduced into the rigid registration. In each iteration, the classical ICP algorithm includes two steps, building the corresponding relationship and computing the rigid transformation. Similar to the traditional ICP, at each step, firstly the corresponding relationship is set up. Secondly, the rigid transformation is solved by singular value decomposition (SVD) method, and then the Gaussian model is updated by the distance and variance between two point sets. The experimental results on part B of CE-Shape-1 database and real position dataset validate that the proposed algorithm is more accurate.