Integrating grid-based and topological maps for mobile robot navigation

  • Authors:

  • Sebastian Thrun;Arno Bü

  • Affiliations:

  • tcomputer Science Department, Carnegie Mellon University, Pittsburgh, PA and Institut für Informatik, Universität Bonn, Bonn, Germany;Institut für Informatik, Universität Bonn, Bonn, Germany

  • Venue:
  • AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
  • Year:
  • 1996

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Abstract

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 considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms--grid-based and topological--, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room environments.