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LOGOS: Local geometric support for high-outlier spatial verification
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0003-3788-499X
Örebro University, School of Science and Technology. (AASS)ORCID iD: 0000-0002-2953-1564
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents LOGOS, a method of spatial verification for visual localization that is robust in the presence of a high proportion of outliers. LOGOS uses scale and orientation information from local neighbourhoods of features to determine which points are likely to be inliers. The inlier points can be used for secondary localization verification and pose estimation. LOGOS is demonstrated on a number of benchmark localization datasets and outperforms RANSAC as a method of outlier removal and localization verification in scenarios that require robustness to many outliers.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018. p. 7262-7269
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-68446ISI: 000446394505077OAI: oai:DiVA.org:oru-68446DiVA, id: diva2:1238392
Conference
IEEE International Conference of Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Note

Funding Agency:

Semantic Robots Research Profile - Swedish Knowledge Foundation (KKS)

Available from: 2018-08-13 Created: 2018-08-13 Last updated: 2018-10-22Bibliographically approved

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Lowry, StephanieAndreasson, Henrik

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