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Fuzzy self-localiza...
Fuzzy self-localization using natural features in the four-legged league
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- Herrero-Pérez, David (author)
- University of Murcia, Spain,Dept. Information and Communication Engineering
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- Martínez-Barberá, Humberto (author)
- University of Murcia, Spain,Dept. Information and Communication Engineering
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- Saffiotti, Alessandro (author)
- Örebro universitet,Institutionen för teknik,Mobile Robotics Lab
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University of Murcia, Spain Dept Information and Communication Engineering (creator_code:org_t)
- Berlin, Heidelberg : Springer Berlin/Heidelberg, 2004
- 2004
- English.
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In: RoboCup 2004. - Berlin, Heidelberg : Springer Berlin/Heidelberg. - 9783540250463 ; , s. 110-121
- Related links:
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http://www.aass.oru....
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Subject headings
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- In the RoboCup four-legged league, robots mainly rely on artificial coloured landmarks for localisation. As it was done in other leagues, artificial landmarks will soon be removed as part of the RoboCup push toward playing in more natural environments. Unfortunately, the robots in this league have very unreliable odometry due to poor modeling of legged locomotion and to undetected collisions. This makes the use of robust sensor-based localization a necessity. We present an extension of our previous technique for fuzzy self-localization based on artificial landmarks, by including observations of features that occur naturally in the soccer field. In this paper, we focus on the use of corners between the field lines. We show experimental results obtained using these features together with the two nets. Eventually, our approach should allow us to migrate from landmarks-only to line-only localisation.
Subject headings
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Keyword
- Autonomous robots
- fuzzy logic
- image processing
- localization
- state estimation
- Datalogi
- Computer and Systems Science
Publication and Content Type
- ref (subject category)
- kon (subject category)
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