language: en

Learning Maps for Indoor Mobile Robot Navigation

Sebastian Thrun
Computer Science Department and Robotics Institute Carnegie Mellon University, Pittsburgh

[Accepted for Publication in Artificial Intelligence]

Abstract Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are often difficult to learn and maintain in large-scale environments, particularly if momentary sensor data is highly ambiguous. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and naive Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms, the approach presented here gains advantages from both worlds: accuracy/consistency and efficiency. The paper gives results for autonomous exploration, mapping and operation of a mobile robot in populated multi-room environments.

[? This research was sponsored in part by the National Science Foundation under award IRI-9313367, and by the Wright Laboratory, Aeronautical Systems Center, Air Force Materiel Command, USAF, and the Darpa Advanced Research Projects Agency (DARPA) under grant number F33615-93-1-1330. We also acknowledge financial support by Daimler Benz Corp. Preprint submitted to Elsevier Science 15 September 1997]