Probability iterative closest point algorithm for m-D point set registration with noise
This paper proposes probability iterative closest point (ICP) method based on expectation maximization (EM) estimation for registration of point sets with noise. The traditional ICP algorithm can deal with rigid registration between two point sets effectively, but it may fail to register point sets with noise. In order to improve the registration precision, a Gaussian model is introduced into the traditional rigid registration problem. At each iterative step, similar to the original ICP algorithm, there are two parts of the proposed method. Firstly, the one-to-one correspondence between two point sets 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 proposed method improves the precision of registration of point sets with noise significantly with fast speed. Experimental results validate that the proposed algorithm is more accurate and faster compared with other rigid registration methods.
iterative closest point point set registration noise gaussian model one-to-one correspondence