Registration Method for Partial Overlapping Point Cloud Data of Complex Workpieces Based on MFD Algorithm

To enhance the efficiency and accuracy of partial-to-whole point cloud registration for complex workpieces, this paper presents a novel method based on the Mean Feature Descriptor (MFD). The proposed approach extracts geometric features from key points in the scanned point cloud, constructs local feature descriptors using a localized coordinate system, and performs coarse registration. Compared to conventional local descriptor-based methods, the MFD algorithm not only effectively captures local geometric characteristics but also significantly improves computational efficiency while maintaining high registration accuracy. Experimental results demonstrate that the MFD-based method substantially accelerates registration and measurement processes for complex workpieces. It exhibits strong robustness against noise and varying point cloud resolutions, outperforming existing descriptors such as PFH, FPFH, and HoPPF in terms of F1 score and matching precision. The method achieves reliable registration even under challenging conditions, such as partial overlap and geometric feature sparsity. Notably, the MFD descriptor inherently captures geometric symmetry-invariant features of local point cloud regions especially symmetric interfaces of complex workpieces. This ensures stable registration performance even when partial scans only preserve part of the workpiece’s symmetric structure.