GFOICP: Geometric Feature Optimized Iterative Closest Point for 3-D Point Cloud Registration
Leping He, Shuaiqing Wang, Qijun Hu, Qijie Cai, Muyao Li, Yu Bai, Kai Wu, Bo Xiang
Abstract
Three-dimensional point cloud registration is a crucial technique for point cloud processing. Iterative closest point (ICP) is widely used for rigid registration of point clouds because of its simplicity but suffers from slow convergence and the tendency to fall into local optimization. In this article, we proposed a new robust and fast point cloud registration method, the ICP optimized by geometric features (including normal, curvature, and point distance), called GFOICP. GFOICP statistically selects registration points by the cross entropy of geometric features of the points, then matches correspondences based on a variable distance threshold, and filters out correct correspondences using an iterative strict constraint on geometric feature similarity. In addition, geometric feature similarity is added as a constraint to the objective function to ensure strict convergence. GFOICP completes registration by iterating correspondence matching, alignment, and convergence judgment. Extensive experiments on publicly available synthetic and real-world datasets have demonstrated that GFOICP significantly improves accuracy and speed compared to standard ICP. GFOICP has similar or higher accuracy and speed than other state-of-the-art registration methods.