MFIPC: Point Cloud Registration Algorithm via Multi-Feature Fusion and Interval Pairing Consistency

Inspired by the Fast point Feature Histogram (FPFH) feature extraction algorithm, this paper proposes a new 3D point cloud registration method, MFIPC (Multi-feature Fusion and Interval Pairing Consistency). The method uses feature fusion and interval pairwise consistency to improve the registration accuracy. In the MFIPC framework, the point cloud is first downsampled to optimize computational efficiency and expand the analysis domain. Then, clustering algorithm using local directional centrality (CDC) classification algorithm is used to calculate the DCM (directional centrality measure) value of each point. The Gaussian curvature values of the points are calculated at the same time, and these eigenvalues are fused. To further refine the registration process, the range between the minimum and maximum eigenvalues is divided into several equal intervals and sorted in ascending order. A sorting algorithm is used to assign each eigenvalue to a corresponding interval. For the global point cloud computing step, after the operation is completed, the number of points in each interval and its proportion are calculated. The program processes both point clouds in order to analyze their interval percentage. This algorithm significantly improves the robustness of MFIPC in establishing point correspondence. To verify the effectiveness of MFIPC for 3D point cloud registration, we conducted extensive testing on various datasets, including 3DMatch, RESSO, ModelNet40, Stanford Rabbit, and Dragon. The experimental results show that the algorithm has high efficiency, good consistency of point cloud, significantly reduced registration errors, low error and high registration accuracy