Registration of 3D Point Clouds Using Mean Shift Clustering on Rotations and Translations

In this paper a novel registration algorithm between 3D point clouds is presented. It exploits the fact that current 3D point descriptors (e.g., RoPS) are accompanied by local reference frames(LRF). LRFs of corresponding points are used to estimate the relative rotation between the point clouds. Thus, inlier matches will generate a cluster of rotation matrices. The size and shape of this cluster is unknown. We therefore develop a mean shift clustering algorithm for noisy rotation matrices. It finds the mode of the distribution to estimate the relative rotation. It is then used for estimating the translation vectors from the matched points. Here again mean shift is used for finding the translation component. The algorithm has been tested on different types of sources of 3D data (3D scanner, Lidar, and Structure from Motion(SfM)) of small scanned objects and urban scenes. In all these cases, the algorithm performed well outperforming state of the art algorithms in accuracy and in speed.

1. Introduction

The alignment of point clouds acquired from several viewpoints, locations and sensors [12] is called 3D registration. 3D registration has numerous applications ranging from 3D object categorization and recognition [17], [9], 3D modeling and scene reconstruction [18], robotic perception [12], etc.