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  Learning view graphs for robot navigation

Franz, M., Schölkopf, B., Mallot, H., & Bülthoff, H. (1998). Learning view graphs for robot navigation. Autonomous Robots, 5(1), 111-125. doi:10.1023/A:1008821210922.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E8B5-B Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-E8B6-9
Genre: Journal Article

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 Creators:
Franz, M1, Author              
Schölkopf, B1, Author              
Mallot, HA2, Author              
Bülthoff, HH2, Author              
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              

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 Abstract: We present a purely vision-based scheme for learning a topological representation of an open environment. The system represents selected places by local views of the surrounding scene, and finds traversable paths between them. The set of recorded views and their connections are combined into a graph model of the environment. To navigate between views connected in the graph, we employ a homing strategy inspired by findings of insect ethology. In robot experiments, we demonstrate that complex visual exploration and navigation tasks can thus be performed without using metric information.

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 Dates: 1998-03
 Publication Status: Published in print
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Title: Autonomous Robots
Source Genre: Journal
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Pages: - Volume / Issue: 5 (1) Sequence Number: - Start / End Page: 111 - 125 Identifier: -