A partially constrained semi-precise registration method for point clouds facing irregular boundaries

Aiming to address the problem of insufficient robustness of traditional point cloud registration algorithms in complex backgrounds, this paper proposes a partially constrained registration method. This method builds a hierarchical optimization framework by organically combining multi-level geometric constraints such as points, lines and surfaces, achieving an efficient transition from coarse registration to semi-fine registration. Firstly, the input point cloud is preprocessed such as denoising and boundary extraction. Then, based on the geometric features of the point cloud (such as size differences and shapes), strategies such as centroid alignment, boundary constraints or corner point matching are adopted, and global alignment is achieved through the multi-constraint fusion optimization model. The experimental results show that in scenario with tens of thousands of point clouds, the centroid registration time of the proposed method is less than 0.5 seconds; the error is 10-20 mm, and the overlap rate exceeds 85%. The time consumption for boundary registration is less than 0.8 seconds, the error is 10-25mm, and the overlap rate is more than 88%. The corner registration time is less than 1.0 second, the error is 8-20mm, and the overlap rate is more than 90%. All indicators are significantly better than traditional algorithms such as RANSAC and ICP. This method effectively improves the accuracy and efficiency of point cloud registration in complex scenarios, providing a reliable preprocessing solution for fields such as industrial inspection and 3D reconstruction.