Feature-based concurrent mapping and localization for AUVs

Abstract

One of the primary problems in marine robot navigation is the growth of uncertainty. Sensory measurements of the environment provide an enticing source of information about vehicle location. Various current approaches to AUV sensor data fusion fall short of incorporating environmental measurements in navigation estimation to improve navigation performance in unmapped environments. We present a unified approach to using environmental measurements to map an unknown environment and localize the vehicle within that map. First, we discuss the importance of our feature-based approach to concurrent mapping and localization (CM&L). Innovative aspects of this algorithm, including feature modeling and decision dependencies, are highlighted. We then present our feature-based CM&L algorithm. Finally, we draw conclusions about the challenges in implementing this algorithm and the performance gains expected for AUV navigation.