Grid graph-based large-scale point clouds registration

Automatic registration of unordered point clouds is the prerequisite for urban reconstruction. However, most of the existing technologies still suffer from some limitations. On one hand, most of them are sensitive to noise and repetitive structures, which makes them infeasible for the registration of large-scale point clouds. On the other hand, most of them dealing with point clouds with limited overlaps and unpredictable location. All these problems make it difficult for registration technology to obtain qualified results in outdoor point cloud. To overcome these limitations, this paper presents a grid graph-based point cloud registration (GGR) algorithm to align pairwise scans. First, point cloud is divided into a set of 3D grids. We propose a voting strategy to measure the similarity between two grids based on feature descriptor, transforming the superficial correspondence into 3D grid expression. Next, a graph matching is proposed to capture the spatial consistency from putative correspondences, and graph matching hierarchically refines the corresponding grids until obtaining point-to-point correspondences. Comprehensive experiments demonstrated that the proposed algorithm obtains good performance in terms of successful registration rate, rotation error, translation error, and outperformed the state-of-the-art approaches.

Automated summary

This paper proposes a grid graph-based point cloud registration algorithm to align unordered point clouds. The algorithm divides the point cloud into a set of 3D grids and uses a voting strategy based on feature descriptors to measure the similarity between two grids. A graph matching method is then proposed to capture spatial consistency and refine the corresponding grids hierarchically to obtain point-to-point correspondences.