Robust low-overlap 3-D point cloud registration for outlier rejection

Abstract

When registering 3-D point clouds it is expected that some points in one cloud do not have corresponding points in the other cloud. These non-correspondences are likely to occur near one another, as surface regions visible from one sensor pose are obscured or out of frame for another. In this work, a hidden Markov random field model is used to capture this prior within the framework of the iterative closest point algorithm. The EM algorithm is used to estimate the distribution parameters and learn the hidden component memberships. Experiments are presented demonstrating that this method outperforms several other outlier rejection methods when the point clouds have low or moderate overlap.