Using local symmetry for landmark selection
2009 (English)In: Computer Vision Systems, Springer , 2009, Vol. 5815, 94-103 p.Chapter in book (Refereed)
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.
, Lecture Notes in Computer Science, ISSN 0302-9743 ; 5815
SLAM, Visual Attention, Symmetry Detection
Computer Vision and Robotics (Autonomous Systems) Robotics
IdentifiersURN: urn:nbn:se:kth:diva-47171DOI: 10.1007/978-3-642-04667-4_10ISBN: 978-3-642-04666-7OAI: oai:DiVA.org:kth-47171DiVA: diva2:454621
QC 2011110092011-11-092011-11-072011-11-09Bibliographically approved