Abstract:

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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.