3D Point Cloud Registration Method by Adaptive Selection of Keypoint Patch Position

Abstract

3D Point cloud registration is the problem of integrating 3D point clouds obtained from multiple viewpoints into a single coordinate axis, and is often used as a preprocessing for modeling and object recognition. In general, highly accurate registration methods require a large computational complexity because the processing is proportional to the number of points in the source point cloud. In order to reduce the amount of computation, the keypoint patches extraction method has been proposed, which uses a point cloud extracted only from the points around the keypoints. However, since the number of points is small, there is a problem that the accuracy of registration will deteriorate if keypoint patches are extracted in nonoverlapping areas. Therefore, we propose a 3D point cloud registration method that adaptively changes the position of keypoint patches. The method extracts the extra keypoint patches in advance and selects only the overlapping parts in those. This operation was implemented by a genetic algorithm that optimizes the overlap parts using the tentative registration results of the selected patches. The experimental results show that the proposed method achieves more accurate registration for point cloud pairs with smaller overlap regions than the conventional method.