  This paper presents a robust probabilistic point registration method for
estimating the rigid transformation (i.e. rotation matrix and translation
vector) between two pointcloud dataset. The method improves the robustness of
point registration and consequently the robot localization in the presence of
outliers in the pointclouds which always occurs due to occlusion, dynamic
objects, and sensor errors. The framework models the point registration task
based on directional statistics on a unit sphere. In particular, a Kent
distribution mixture model is adopted and the process of point registration has
been carried out in the two phases of Expectation-Maximization algorithm. The
proposed method has been evaluated on the pointcloud dataset from LiDAR sensors
in an indoor environment.
