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Haar invariant signatures and spatial recognition using omnidirectional visual information only

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Abstract

This paper describes a method for spatial representation, place recognition and qualitative self-localization in dynamic indoor environments, based on omnidirectional images. This is a difficult problem because of the perceptual ambiguity of the acquired images, and their weak robustness to noise, geometrical and photometric variations of real world scenes. The spatial representation is built up invariant signatures using Invariance Theory where we suggest to adapt Haar invariant integrals to the particular geometry and image transformations of catadioptric omnidirectional sensors. It follows that combining simple image features in a process of integration over visual transformations and robot motion, can build discriminant percepts about robot spatial locations. We further analyze the invariance properties of the signatures and the apparent relation between their similarity measures and metric distances. The invariance properties of the signatures can be adapted to infer a hierarchical process, from global room recognition to local and coarse robot localization.

The approach is validated in real world experiments and compared to some local and global state-of-the-art methods. The results demonstrate a very interesting performance of the proposed approach and show distinctive behaviors of global and local methods. The invariant signature method, while being very time and memory efficient, provides good separability results similarly to approaches based on local features.

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Authors and Affiliations

  1. Model, Information and Systems Laboratory, University of Picardie Jules Verne, 33 rue Saint Leu, Amiens, 80000, France

    Ouiddad Labbani-Igbida, Cyril Charron & El Mustapha Mouaddib

Authors
  1. Ouiddad Labbani-Igbida
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  2. Cyril Charron
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  3. El Mustapha Mouaddib
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Correspondence to Ouiddad Labbani-Igbida.

Additional information

This work was supported by Region of Picardie under the DIVA program.

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Labbani-Igbida, O., Charron, C. & Mouaddib, E.M. Haar invariant signatures and spatial recognition using omnidirectional visual information only. Auton Robot 30, 333–349 (2011). https://doi.org/10.1007/s10514-011-9222-7

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  • DOI: https://doi.org/10.1007/s10514-011-9222-7

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