PYRF-PCR: A Robust Three-Stage 3D Point Cloud Registration for Outdoor Scene

Point Cloud Registration (PCR) has been viewed as an essential part of photogrammetry, remote sensing, and autonomous robot mapping. Existing methods are either sensitive to rotation transformations, or rely on feature learning networks with poor generalization. We propose a novel outdoor point cloud registration algorithm, including preprocessing, yaw angle estimation, coarse registration, and fine registration (in short, PYRF-PCR). Specifically, the preprocessing effectively eliminates the interference of ground point clouds to PCR. The proposed yaw angle estimator solves large yaw-angle matching via a cross-correlation function that converts the focus from the yaw angle estimation to the LiDAR horizontal angular resolution analyses. Then, by using frequency distribution histograms, we improve the fast point feature histogram algorithm to filter the point clouds with a more stable density. For the fine registration, an improved iterative closest point based on target centroid distance is proposed, which reduces the running time and the search range between two point clouds. To validate the widespread applicability of PYRF-PCR, we experimented on both the open-source dataset (KITTI) and the local campus scene dataset. On the KITTI dataset, experimental results illustrate that the PYRF-PCR can achieve state-of-the-art results compared with the existing best methods. On the local scene datasets, the higher quality matching in different types of target point clouds reflects the generalization ability of PYRF-PCR.