Neural model of a grid-based map for robot sonar

Abstract

A functional similarity is described between cells of an occupancy grid for robot sonar, and integrate-and-fire neurons of an artificial neural net. Using this analogy, a new grid-based mapping system for robot sonar is described, which makes use of the neural concepts of receptive fields and recurrent connections. The performance of the new network is compared to that of a previous Bayesian grid-based mapping method, and a previous feature-based mapping method.