Integrating exploration and localization for mobile robots

Exploration and localization are two of the capabilities necessary for mobile robots to navigate robustly in unknown environments. A robot needs to explore in order to learn the structure of the world, and a robot needs to know its own location in order to make use of its acquired spatial information. However, a problem arises with the integration of exploration and localization. A robot needs to know its own location in order to add new information to its map, but a robot may also need a map to determine its own location. We have addressed this problem with ARIEL, a mobile robot system that combines frontier-based exploration with continuous localization. ARIEL is capable of exploring and mapping an unknown environment while maintaining an accurate estimate of its position at all times. In this paper, we describe frontier-based exploration and continuous localization, and we explain how ARIEL integrates these techniques. Then we show results from experiments performed in the exploration of a real-world office hallway environment. These results demonstrate that maps learned using exploration without localization suffer from substantial dead reckoning errors, while maps learned by ARIEL avoid these errors and can be used for reliable exploration and navigation.