Novel automated methods for coarse and fine registrations of point clouds in high precision metrology

Abstract

Several measuring systems can be combined to perform accurate assessments at the sub-micrometer level in dimensional metrology. The obtained data are fused into a common coordinate system using registration methods for which the optimal transformation parameters from the common parts of the data called correspondences are computed. New original automated coarse and fine registration methods are proposed here using discrete curvatures: an improved Hough transformation method for the coarse registration and three iterative closest point (ICP) variants for the fine registration. The enhancement of Hough consists of exploiting the curvature parameters in order to minimize the basic algorithm complexity. Thus, local transformation parameters are only computed for points presenting similar precalculated surface type. While the rough alignment of the scene data and the model data is thereafter optimized through the fine registration using common ICP algorithm, the first ICP variant includes the curvedness and surface type similarity constraints, especially to reduce the searching area during the matching step. For the proposed second ICP variant, correspondences are searched using a specific distance criterion involving curvature feature similarity measure defined from principal curvatures. The third ICP variant combines both point-to-point and point-to-plane minimizations automatically weighted in the objective function, with the use of moving least squares (MLS) surface technique to determine the corresponding point in point-to-point part. The three developed methods are tested on simulated and real data obtained from a computer tomography (CT) system. The results reveal the benefit of the proposed new automated coarse and fine registration approaches.