Gradient-based local affine invariant feature extraction for mobile robot localization in indoor environments

In this paper, we propose a gradient-based local affine invariant feature extraction algorithm (G-LAIFE), using affine moment invariants for robot localization in real indoor environments. The proposed algorithm is an effective feature extraction algorithm that is invariant to image translation and...

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Bibliographic Details
Published in:Pattern recognition letters 2008-10, Vol.29 (14), p.1934-1940
Main Authors: Lee, Jihyo, Ko, Hanseok
Format: Article
Language:English
Subjects:
3D rotation
Affine invariant
Applied sciences
Exact sciences and technology
Information, signal and communications theory
Local feature extraction
Miscellaneous
Signal processing
Telecommunications and information theory
Translation
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Summary:In this paper, we propose a gradient-based local affine invariant feature extraction algorithm (G-LAIFE), using affine moment invariants for robot localization in real indoor environments. The proposed algorithm is an effective feature extraction algorithm that is invariant to image translation and to 3D rotation, and it is within a partial range of the image scale. Representative performance analysis confirms that the proposed G-LAIFE algorithm significantly enhances the recognition rate and is more efficient than the scale invariant feature transform (SIFT), especially in terms of 3D rotation change and computational time.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2008.06.006