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Kootstra, Geert; de Jong, Sjoerd; Schomaker, Lambert R. B. (2009)
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: SLAM, Visual Attention, Computer Vision and Robotics (Autonomous Systems), Symmetry Detection, Datorseende och robotik (autonoma system), Robotteknik och automation, Robotics

Classified by OpenAIRE into

arxiv: Computer Science::Robotics, Computer Science::Computer Vision and Pattern Recognition
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. QC 201111009
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