Fully automated registration of 3D CT data to CAD model for surface deviation measurement

The aim of this study is to present a fully automated registration algorithm that allows for alignment and errors analysis of the 3D surface model obtained from industrial computed tomography (CT) images with the computer-aided design (CAD) model. First, two pre-processing steps are executed by the algorithm namely, CAD model subdivision and representing models. Next, two improved registration procedures are applied including covariance descriptors-based coarse registration with a novel and automatic calibration, followed by a fine registration technique that utilizes an improved iterative closest points (ICP) algorithm, which is what we proposed with a novel estimation method for registration error. Finally, using a novel strategy that we proposed for error display, the quantitative data analysis results can simultaneously estimate both positive and negative deviation of the surface registration errors more precisely and fully expressed. Comparing to the original ICP algorithm, the quantitative data of experimental results demonstrate that the average registration errors of carburetors and valves are reduced by 0.80 millimeter at least. Therefore, this study demonstrates that the proposed new algorithm is not only capable of fully automating the registration of 3D surface model to a CAD model but also beneficial for quantitatively determining the surface manufacturing error more precisely.