Fast and Robust Isotropic Scaling Probability Iterative Closest Point Algorithm

This paper proposes a new probability iterative closest point approach with bounded scale based on expectation maximization (EM) estimation for scaling registration of point sets with noise. The bounded scale ICP algorithm can handle the case with different scales, but it could not effectively yield the alignment of point sets with noise. Aiming at improving the registration precision, a Gaussian probability model is integrated into the bounded scale registration. The proposed method can be solved by the E-step and M-step. In the E-step, we can build up the one-to-one correspondence between two point sets. In the M-step, the scale transformation which consists of the rotation matrix, translation vector, and the scale factor is solved by singular value decomposition (SVD) method and the properties of parabola. Then, the Gaussian model is updated via the distance and variance between the transformed point sets. As one-to-one correspondence is adopted for the scaling registration of point sets with noise, the proposed method improves the performance significantly with high precision and fast speed. Experimental results demonstrate that the proposed algorithm is more accurate and fast.