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Using multiple gaussian hypotheses to represent probability distributions for mobile robot localization
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0002-1170-7162
2000 (English)Conference paper, Published paper (Refereed)
Abstract [en]

A new mobile robot localization technique is presented which uses multiple Gaussian hypotheses to represent the probability distribution of the robots location in the environment. A tree of hypotheses is built by the application of Bayes' rule with each new sensor mesurement. However, such a tree can grow without bound and so rules are introduced for the elimination of the least likely hypotheses from the tree and for the proper re-distribution of their probability. This technique is applied to a feature-based mobile robot localization scheme and experimental results are given demonstrating the effectiveness of the scheme.

Place, publisher, year, edition, pages
2000. 1036-1041 p.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-52980OAI: oai:DiVA.org:kth-52980DiVA: diva2:468363
Conference
Proc. of the IEEE International Conference on Robotics and Automation (ICRA’00)
Note
NR 20140805Available from: 2011-12-20 Created: 2011-12-20Bibliographically approved

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Jensfelt, Patric

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