Integrating Grid-Based and Topological Maps for Mobile Robot Navigation

Sebastian Thrun and Arno Buecken

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.

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@INPROCEEDINGS{Thrun96d,
  AUTHOR         = {S. Thrun and A. B\"{u}cken},
  YEAR           = {1996},
  TITLE          = {Integrating Grid-Based and Topological Maps for 
                    Mobile Robot Navigation},
  BOOKTITLE      = {Proceedings of the AAAI Thirteenth National 
                    Conference on Artificial Intelligence},
  ADDRESS        = {Portland, Oregon}
}