Design and test of a board for CNN-based stereo vision

One of the most essential requirements in robotic autonomous navigation is the extraction of three-dimensional information about the environment in order to avoid collisions with moving or fixed obstacles. Among the others, one of the most promising approaches for this task is represented by the techniques of artificial vision. Several implementations of different approaches have been proposed in many papers in literature. In particular, the authors presented an implementation of the stereo vision algorithm using cellular neural networks. In this paper, the design of an electronic board with dedicated CNN analogue chips able to implement the algorithm is presented.