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  Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements

Franchi, A., Oriolo, G., & Stegagno, P. (2013). Mutual Localization in Multi-Robot Systems using Anonymous Relative Measurements. International Journal of Robotics Research, 32(11), 1302-1322. doi:10.1177/0278364913495425.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-001A-14BE-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-3DD9-7
Genre: Journal Article

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 Creators:
Franchi, A1, 2, Author              
Oriolo, G, Author
Stegagno, P, Author              
Affiliations:
1Department Human Perception, Cognition and Action, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497797              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: We propose a decentralized method to perform mutual localization in multi-robot systems using anonymous relative measurements, i.e. measurements that do not include the identity of the measured robot. This is a challenging and practically relevant operating scenario that has received little attention in the literature. Our mutual localization algorithm includes two main components: a probabilistic multiple registration stage, which provides all data associations that are consistent with the relative robot measurements and the current belief, and a dynamic filtering stage, which incorporates odometric data into the estimation process. The design of the proposed method proceeds from a detailed formal analysis of the implications of anonymity on the mutual localization problem. Experimental results on a team of differential-drive robots illustrate the effectiveness of the approach, and in particular its robustness against false positives and negatives that may affect the robot measurement process. We also provide an experimental comparison that shows how the proposed method outperforms more classical approaches that may be designed building on existing techniques. The source code of the proposed method is available within the MLAM ROS stack.

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 Dates: 2013-09
 Publication Status: Published in print
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 Identifiers: DOI: 10.1177/0278364913495425
BibTex Citekey: FranchiOS2013
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Title: International Journal of Robotics Research
Source Genre: Journal
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Pages: - Volume / Issue: 32 (11) Sequence Number: - Start / End Page: 1302 - 1322 Identifier: -