Multimodal real-world mapping and navigation system for autonomous mobile robots based on neural maps

Abstract

This work describes a neural network-based approach to multimodal real-world mapping and navigation for autonomous mobile robots in unknown environments. The system is built on top of a vector associative map to combine range data from stereo vision and ultrasonic rangefinders. Visual output from a boundary contour system is used to extract range data from a pair of 2-D images. In addition, range data from ultrasonic lasers is used to eliminate uncertainties, noise, and intrinsic errors introduced by the measurements. A recurrent competitive field used to model multimodal working memory excites a trajectory formation network which transforms desired temporal patterns (i.e., a trajectory formation pattern) into spatial patterns. The output of this network is processed by direction-sensitive cells which in turn activates the motor system that guides a mobile robot in unstructured environments. The model is capable of unsupervised, real-time, fast error-based learning of an unstructured environment.