[Paper] Sparse Group Match for Point Cloud Registration

Abstract

This paper proposes a point groups-based algorithm for point cloud registration. Most of the existing algorithms align two point clouds globally; however, they are unsuitable when the overlapping ratio is low or the inputs do not have strong features. The high accuracy of matched points is conducive for a rigid transformation of point clouds. This study aims to determine the exact matching points to register point clouds. The proposed method is based on point groups that are resampled point clouds. Subsequently, we calculate the multiple average probability (MAP) for each point group and match them by a sparse representation. Finally, the coherent point drift (CPD) algorithm is used to register the matched point groups, and the same transformation is applied to register the point clouds. The experimental results show that in terms of robustness to noise and outliers, our algorithm can register point clouds with a low overlapping ratio.

[Paper] Double Sparse Representation for Point Cloud Registration

“…However, the point group is a small part of the point cloud, and it can effectively improve the robustness of registration. Our previous work 12) provides such an example. This method mainly divides the point cloud registration into four steps.…”