An artificial intelligence approach to registration of free-form shapes

Registration, defined as the process of matching geometric entities, is performed when multiple scanned data sets must be aligned or when an existing model must match digitized point clouds. This process is crucial in several applications such as Reverse Engineering, CAD-based inspection and computer vision. The goal of this process is the computation of the optimal rigid transformation for the alignment of several sets of geometric entities (points and/or surfaces). Registration is generally performed by using a two-step procedure necessary to realize coarse and fine alignments. Human intervention is normally required for coarse registration while fine registration is usually a semi-automatic procedure. Consequently alignment is not usually a single step automatic operation and is also affect by errors. In this paper the authors propose a hybrid approach for automatic registration applied to free-formshapes. This hybrid approach employs a asynchronous data communication between an Artificial Neural Network and Genetic Algorithms. The Neural Network performs the coarse alignment giving an initial solution for the registration operation which is then performed by Genetic Algorithms to minimize error deviations between geometrical entities. Several case studies have been investigated in order to validate the proposed approach.