Semirigid optimal step iterative algorithm for point cloud registration and segmentation in grid structure deformation detection

Abstract

Deformation detection of grid structures is vital. In complex environments, efficiently identifying locally crooked members among tens of thousands remains a significant challenge. Point cloud-based methods provide dependable solutions for instance segmentation and deformation recognition. However, existing approaches struggle with irrelevant and deficient data, diverse component forms, and low efficiency. This paper introduces non-rigid registration to grid structure scenarios and proposes a semirigid optimal step iterative point cloud registration and segmentation algorithm (SOSIT), specifically designed for grid structures. By leveraging geometric and physical priors, including the as-designed model topology, plane section and finite rotation assumptions, along with differential stiffness and stepwise softening constraints, SOSIT addresses critical challenges in spatial topology organization, transformation matrix representation, and spatially dependent stiffness variation. The algorithm achieves state-of-the-art (SOTA) performance, with a 129-fold increase in efficiency, a 10.1 % improvement in accuracy, and an 81.5 % enhancement in robustness, enabling automated and intelligent deformation inspection and monitoring.