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Learning Metric-Topological Maps for Indoor Mobile Robot Navigation
Type of publication: Article
Citation: thrun_98_learning
Journal: Artificial Intelligence
Volume: 99
Number: 1
Year: 1998
Pages: 21-71
Publisher: Elsevier
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. The 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 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.
Userfields: date-added={2012-09-03 15:47:30 +0200}, date-modified={2012-09-03 15:47:30 +0200}, notes={ISSN: 0004-3702}, project={fremdliteratur}, registry={A21 E18}, state={read},
Keywords: accuracy, artificial neural networks, autonomous exploration, consistency, efficiency, environments, grid-based methods, indoor mobile robot navigation, large-scale environments, LEARNING (ARTIFICIAL INTELLIGENCE), mapping, mobile robots, momentary sensor data, naive Bayesian integration, neural nets, PATH PLANNING, populated multi-room environments, topological maps, TOPOLOGY metric-topological maps
Authors Thrun, Sebastian