Fast and robust registration of multiple 3D point clouds

Abstract

In this paper, we propose a method for the fast and robust registration of multiple 3D point clouds. This method contributes the simultaneous localization and mapping (SLAM) robot. The problem of point cloud registration can be roughly classified into two. The problem of estimation of the relation from unknown geometry position is coarse registration. Moreover, in case of roughly position is known, the problem of estimation of the relation from known initial position is fine registration. In this study, we solve the coarse registration problem by exhaustive search, and prepare the initial positions for fine registration. To employ exhaustive search, we realize robust matching by using no feature point. The fine registration problem is solved by the ICP algorithm. In this case, reduction of initial positions for the ICP algorithm by non-extremum suppression that uses distance between two range data solves the problem of calculation cost. The distance evaluation function that robust for measurement error tackles the outlier problem. The problem of calculation cost is solved by registering the distance from the last point cloud data beforehand by the distance field. The effectiveness of the proposed method is shown by a actual data.