Freeform surface mechanical part dimensional inspection based on a novel gravitational field feature description and registration approach

Abstract Traditional point cloud registration-based methods enable rapid dimensional inspection of complex geometries but are sensitive to the initial poses and noise. This paper proposes a novel point cloud gravitational field theory (GFT) feature description and registration method to enhance inspection accuracy. A local coordinate system is constructed using principal eigenvectors to achieve coarse alignment. The point mass is defined based on the surface curvature. A multi-dimensional rotation-invariant GFT feature descriptor is designed, including gravitational force, field strength, potential energy and mass density. The correspondence relationship is established according to the GFT feature similarities and a transformation matrix is computed by singular value decomposition for fine registration. The experimental results demonstrate that the proposed method significantly reduces registration errors on complex freeform surfaces such as wind turbine blades, car exteriors and turbine blades, achieving error reductions of 0.4%–27.9%.