Using local symmetry for landmark selection

Gert Kootstra*, Sjoerd De Jong, Lambert R.B. Schomaker

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper


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.

Original languageEnglish
Title of host publicationComputer Vision Systems - 7th International Conference, ICVS 2009, Proceedings
EditorsMario Fritz, Bernt Schiele, Justus H. Piater
Number of pages10
ISBN (Print)9783642046667
Publication statusPublished - 14 Dec 2009
Externally publishedYes
Event7th International Conference on Computer Vision Systems, ICVS 2009 - Liege, Belgium
Duration: 13 Oct 200915 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5815 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Computer Vision Systems, ICVS 2009

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