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ACM Computing Surveys (CSUR)
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
The spatial semantic hierarchy
Artificial Intelligence
Robot Motion Planning
Modelling Navigational Knowledge by Route Graphs
Spatial Cognition II, Integrating Abstract Theories, Empirical Studies, Formal Methods, and Practical Applications
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SFCS '79 Proceedings of the 20th Annual Symposium on Foundations of Computer Science
Computing a representation of the local environment
Artificial Intelligence
Specification of an ontology for route graphs
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
Towards dialogue based shared control of navigating robots
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
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International Journal of Robotics Research
Exploiting qualitative spatial constraints for multi-hypothesis topological map learning
COSIT'09 Proceedings of the 9th international conference on Spatial information theory
Voronoi graph matching for robot localization and mapping
Transactions on computational science IX
Voronoi graph matching for robot localization and mapping
Transactions on computational science IX
Specification of an ontology for route graphs
SC'04 Proceedings of the 4th international conference on Spatial Cognition: reasoning, Action, Interaction
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A route graph as proposed in Werner et al. (2000) is a spatial representation of the environment that focuses on integrating qualitatively different routes an agent can use for navigation. In this paper we describe how a route graph based on the generalized Voronoi diagram (GVD) of the environment can be used for mobile robot mapping and navigation tasks in an office-like indoor environment. We propose a hierarchical organization of the graph structure resulting in more abstract layers that represent the environment at coarser levels of granularity. For this purpose, we define relevance measures to weight the meet points in the GVD based on how significant they are for navigation and present an algorithm that utilizes these weights to generate the coarser route graph layers. Computation of the relevance values from either complete or incomplete information about the environment is considered. Besides robot navigation, the techniques developed can be employed for other tasks in which abstract route graph representations are advantageous, e.g. automatically generating route descriptions from floor plans.