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Visual Recognition of Workspace Landmarks for Topological Navigation

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Abstract

In this work, robot navigation is approached using visual landmarks. Landmarks are not preselected or otherwise defined a priori; they are extracted automatically during a learning phase. To facilitate this, a saliency map is constructed on the basis of which potential landmarks are highlighted. This is used in conjunction with a model-driven segregation of the workspace to further delineate search areas for landmarks in the environment. For the sake of robustness, no semantic information is attached to the landmarks; they are stored as raw patterns, along with information readily available from the workspace segregation. This subsequently facilitates their accurate recognition during a navigation session, when similar steps are employed to locate landmarks, as in the learning phase. The stored information is used to transform a previously learned landmark pattern, according to the current position of the observer, thus achieving accurate landmark recognition. Results obtained using this approach demonstrate its validity and applicability in indoor workspaces.

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Authors and Affiliations

  1. Foundation for Research and Technology—Hellas (FORTH), Institute of Computer Science, P.O. Box 1385, Heraklion, 711 10, Crete, Greece;

    Panos E. Trahanias, Savvas Velissaris & Stelios C. Orphanoudakis

  2. Department of Computer Science, University of Crete, P.O. Box 1470, Heraklion, 714 09, Crete, Greece

    Panos E. Trahanias, Savvas Velissaris & Stelios C. Orphanoudakis

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  1. Panos E. Trahanias
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  2. Savvas Velissaris
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  3. Stelios C. Orphanoudakis
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Trahanias, P.E., Velissaris, S. & Orphanoudakis, S.C. Visual Recognition of Workspace Landmarks for Topological Navigation. Autonomous Robots 7, 143–158 (1999). https://doi.org/10.1023/A:1008910100968

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