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Using local symmetry for landmark selection
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
Faculty of Mathematics and Natural Sciences, University of Groningen, The Netherlands. (Artificial Intelligence)
2009 (English)In: Computer Vision Systems, Springer , 2009, Vol. 5815, 94-103 p.Chapter in book (Refereed)
Abstract [en]

Most visual Simultaneous Localization And Mapping (SLAM) methods use interest points as landmarks in their maps of the environment. Often the interest points are detected using contrast features, for instance those of the Scale Invariant Feature Transform (SIFT). The SIFT interest points, however, have problems with stability, and noise robustness. Taking our inspiration from human vision, we therefore propose the use of local symmetry to select interest points. Our method, the MUlti-scale Symmetry Transform (MUST), was tested on a robot-generated database including ground-truth information to quantify SLAM performance. We show that interest points selected using symmetry are more robust to noise and contrast manipulations, have a slightly better repeatability, and above all, result in better overall SLAM performance.

Place, publisher, year, edition, pages
Springer , 2009. Vol. 5815, 94-103 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 5815
Keyword [en]
SLAM, Visual Attention, Symmetry Detection
National Category
Computer Vision and Robotics (Autonomous Systems) Robotics
Identifiers
URN: urn:nbn:se:kth:diva-47171DOI: 10.1007/978-3-642-04667-4_10ISBN: 978-3-642-04666-7 (print)OAI: oai:DiVA.org:kth-47171DiVA: diva2:454621
Note
QC 201111009Available from: 2011-11-09 Created: 2011-11-07 Last updated: 2011-11-09Bibliographically approved

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