Pose estimation in automated visual inspection using ANN

We present an accurate and robust pose estimator of rigid, polyhedral objects, based on artificial neural networks (ANN), suitable for automated visual inspection applications. The estimator is novel in the sense that it is trained with different poses of the objects having dimensional deviations and is therefore robust with respect to dimensional errors. The estimation accuracy is scalable and our computer simulation experiments in the existing configurations of ANNs have shown an accuracy better than 4% of the placement error. The ANN based pose estimator offers several advantages over the classical implementations.