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Learning Maps for Indoor Mobile Robot Navigation (1998)
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| Venue: | Artificial Intelligence |
| Citations: | 92 - 10 self |
BibTeX
@ARTICLE{Thrun98learningmaps,
author = {Sebastian Thrun},
title = {Learning Maps for Indoor Mobile Robot Navigation},
journal = {Artificial Intelligence},
year = {1998},
volume = {99},
pages = {21--71}
}
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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 ...
Keyphrases
indoor mobile robot navigation grid-based map topological map grid-based method naive bayesian integration mobile robot navigation major paradigm accurate metric map autonomous robot consistent topological map indoor environment momentary sensor data efficient planning large-scale indoor environment gain advantage problem solving large-scale environment coherent region artificial neural network
