<title>Neural networks for mobile robot visual exploration</title>

Abstract

This work describes the implementation of some of the neural systems that will enable a mobile robot to actively explore and learn its environment visually. These systems perform the real-time extraction of robust visual features, the segmentation of landmarks from the background and from each other using binocular attentional mechanisms, the predictive binocular tracking of landmarks, and the learning and recognition of landmarks from their features. Also described are preliminary results of incorporating most of these systems into a mobile robot called MAVIN, which can demonstrate the visual exploration of simplified landmarks. Finally, we discuss plans for using similar neural strategies to extend MAVIN's capabilities by implementing a biologically plausible system for navigating through an environment that has been learned by exploration. This explorational learning consists of quantizing the environment into orientation-specific place fields generated by the view-based spatial distribution of landmarks, and associating these place fields in order to form qualitative, behavioral, spatial maps.