Fast and Accurate Registration of Structured Point Clouds with Small Overlaps

Abstract

To perform registration of structured point clouds with large rotation and small overlaps, this paper presents an algorithm based on the direction angles and the projection information of dense points. This algorithm fully employs the geometric information of structured environment. It consists of two parts: rotation estimation and translation estimation. For rotation estimation, a direction angle is defined for a point cloud and then the rotation matrix is obtained by comparing the difference between the distributions of angles. For translation estimation, the point clouds are projected onto three orthogonal planes and then a correlation operation is performed on the projection images to calculate the translation vector. Experiments have been conducted on several datasets. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches in terms of both accuracy and efficiency.