Elsevier

Artificial Intelligence

Volume 99, Issue 1, February 1998, Pages 21-71
Artificial Intelligence

Learning metric-topological maps for indoor mobile robot navigation

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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.

Keywords

Autonomous robots
Exploration
Mobile robots
Neural networks
Occupancy grids
Path planning
Planning
Robot mapping
Topological maps

Cited by (0)

This research is sponsored in part by Daimler-Benz Research (via Frieder Lohnert), the National Science Foundation under award IRI-9313367, 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, and the Defense Advanced Research Projects Agency (DARPA) via the Air Force Missile System Command under contract number F04701-97-C-0022. The views and conclusions contained in this document are those of the author and should not be interpreted as necessarily representing official policies or endorsements, either expressed or implied, of Daimler-Benz Research, the National Science Foundation, the Air Force Materiel Command, the Air Force Missile System Command, or the United States Government.