A Neural Algorithm for Object Positioning in 3D Space Using Optoelectronic System
Automatic object positioning in 3D space is nowadays required by a great variety of applications. We propose here a new approach to this problem, whose core is constituted by a bank of neural networks; from the measured positions of a set of laser spots generated on the object surface, the nets estimate the position of a set of points rigidly connected to the object. Results on synthetic data are reported, and show that the proposed method is reliable and comparable in accuracy with the most common solutions present in the literature, which are based on Iterative Closest Point (ICP) matching.
KeywordsObject positioning 3D space neural bank
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