Cellular automata based inverse perspective transform as a tool for indoor robot navigation

In this paper we describe a system for perspective-effect removal using the cellular automata paradigm. The system has been developed as a tool to be included in the vision system of an autonomous robot designed to operate in indoor environments. The navigation of such a robot is guided by traffic signs by means of a neural-network based system. By using such a tool as part of the vision system, it is possible to instruct the neural networks with a training set including only frontal views of the signs, thus simplifying training and making it computationally much lighter. Furthermore, using a low-cost massively parallel architecture implementing the cellular automata paradigm, makes the algorithm much more computationally efficient with respect to sequential implementations, thus making it possible for the robot to perform just-in-time operations.