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SVM-based discriminative accumulation scheme for place recognition
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.ORCID iD: 0000-0002-1396-0102
2008 (English)In: 2008 IEEE International Conference On Robotics And Automation, Vols 1-9, 2008, 522-529 p.Conference paper, Published paper (Refereed)
Abstract [en]

Integrating information coming from different sensors is a fundamental capabitity for autonomous robots. For complex tasks like topological localization, it would be desirable to use multiple cues, possibly from different modalities, so to achieve robust performance. This paper proposes a new method for integrating multiple cues. For each cue we train a large margin classifier which outputs a set of scores indicating the confidence of the decision. These scores are then used as input to a Support Vector Machine, that learns how to weight each cue, for each class, optimally during training. We call this algorithm SVM-based Discriminative Accumulation Scheme (SVM-DAS). We applied our method to the topological localization task, using vision and laser-based cues. Experimental results clearly show the value of our approach.

Place, publisher, year, edition, pages
2008. 522-529 p.
Series
IEEE International Conference On Robotics And Automation, ISSN 1050-4729
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-38785DOI: 10.1109/ROBOT.2008.4543260ISI: 000258095000083Scopus ID: 2-s2.0-51649129068ISBN: 978-1-4244-1646-2 (print)OAI: oai:DiVA.org:kth-38785DiVA: diva2:438190
Conference
2008 IEEE International Conference on Robotics and Automation, ICRA 2008; Pasadena, CA; 19 May 2008 through 23 May 2008
Available from: 2011-09-01 Created: 2011-08-31 Last updated: 2011-09-01Bibliographically approved

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Pronobis, Andrzej

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf