6DOF iterative closest point matching considering a priori with maximum a posteriori estimation

We present a new matching algorithm considering a priori (the prior probability) based on Bayes' theorem. Performance of point cloud registration between target and source clouds is effectively improved by introducing maximum a posteriori (MAP) estimation. The standard Iterative Closest Point (ICP) algorithm for the registration sometimes falls into misalignment due to measurement errors, narrow sensing field of view, or the movement of objects during measurement. Our approach resolves such problems by considering both the likelihood of the measurement and the prior probability of the initial guess for registration in the objective function. We have implemented a new 6DOF Iterative Closest Point matching using MAP estimation, and evaluated the method in real environments comparing with conventional registration methods. The experimental results have shown that our proposed method has wide convergence region and matches point clouds accurately preventing the misalignment problem.