4 February 2013 Loop closure detection using local Zernike moment patterns
Author Affiliations +
Proceedings Volume 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques; 866207 (2013); doi: 10.1117/12.2008473
Event: IS&T/SPIE Electronic Imaging, 2013, Burlingame, California, United States
This paper introduces a novel image description technique that aims at appearance based loop closure detection for mobile robotics applications. This technique relies on the local evaluation of the Zernike Moments. Binary patterns, which are referred to as Local Zernike Moment (LZM) patterns, are extracted from images, and these binary patterns are coded using histograms. Each image is represented with a set of histograms, and loop closure is achieved by simply comparing the most recent image with the images in the past trajectory. The technique has been tested on the New College dataset, and as far as we know, it outperforms the other methods in terms of computation efficiency and loop closure precision.
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Evangelos Sariyanidi, Onur Sencan, Hakan Temeltas, "Loop closure detection using local Zernike moment patterns", Proc. SPIE 8662, Intelligent Robots and Computer Vision XXX: Algorithms and Techniques, 866207 (4 February 2013); doi: 10.1117/12.2008473; http://dx.doi.org/10.1117/12.2008473

Binary data

Image processing




Computer vision technology

Machine vision

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