Enhanced revolve ICP: robust registration of low-overlap helical structures via single-degree-of-freedom optimization

Abstract The registration of spiral-shaped three-dimensional structures is critical in engineering and biomedical fields. To address the challenging problem of registering spiral point clouds with low overlap, the Revolve ICP algorithm is proposed. By employing a single-degree-of-freedom point-to-point registration framework and dynamically adjusting the nearest-neighbor search threshold, the complex matrix decomposition issue is simplified to a single-variable optimization problem, thereby enhancing robustness while reducing computational costs. A PCA-based axis-fitting method is integrated for point cloud pre-alignment. Simulation and experimental results demonstrate that the proposed algorithm achieves significantly higher accuracy in regis-tering spiral rotational bodies compared to traditional methods, successfully achieving up to 100% overlap rates in applica-tions such as worm point cloud registration.