An advanced method for matching partial 3D point clouds to free-form CAD models for in-situ inspection and repair

3D scanning is a key process in the fields of robotics and computer vision, and can be used for shape comparison between real-world features and Computer-Aided Design (CAD) models. In order to be beneficial, algorithms must be able to accurately register the 3D point cloud to the model surface. In this paper, we propose a registration method using Correlation Coefficients of the point cloud dimensions along with pose calculated by Procrustes Analysis to provide an ideal global registration for the Iterative Closest Point (ICP) algorithm. The method is analysed in the context of objects that are largely smooth and featureless, with only partial scans captured of the objects’ surfaces, and improves the accuracy of registration in such scenarios. This work has resulted in registrations with more accuracy in cases where a rotational alignment is known but a specific position cannot be identified, than if either ICP or Procrustes are used individually, or when Procrustes is used to provide an initial transformation to ICP. It is shown that by applying the proposed method to partially scanned objects, the Root Mean Square Error (RMSE) is significantly reduced. The method is compared with the SAC-IA alignment algorithm, implemented in the Point Cloud Library (PCL), and the results show 0.4mm RMSE for the proposed method and 24.5mm RMSE for the SAC-IA with ICP. The findings in this work could be used in industrial applications including in-situ robotic repair and inspection of free-form manufactured parts.

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