PCGOR: A Novel Plane Constraints-Based Guaranteed Outlier Removal Method for Large-Scale LiDAR Point Cloud Registration

Point cloud registration is a crucial challenge in photogrammetry and computer vision, aimed at aligning adjacent point clouds optimally. In this article, we present a novel registration approach based on plane constraints for large-scale LiDAR point clouds, called PCGOR, effectively decoupling rotation estimation and translation estimation. The point cloud registration challenge is then bifurcated into two distinct parts: rotation estimation and translation estimation. For rotation estimation, we develop an outlier removal method combining coarse filtering with rotation-invariant constraints (RICs) and refined filtering based on computational geometric consistency checks, effectively pruning outliers and robustly estimating accurate relative rotations from plane normals. In translation estimation, we design a componentwise approach based on translation component constraints (TCCs) to efficiently estimate relative translations. Experimental findings validate the robustness and efficacy of our proposed approach on three popular LiDAR point cloud datasets, yielding state-of-the-art performance.