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Knowledge-based multi-criteria optimization to support indoor positioning

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Abstract

Indoor position estimation constitutes a central task in home-based assisted living environments. Such environments often rely on a heterogeneous collection of low-cost sensors whose diversity and lack of precision has to be compensated by advanced techniques for localization and tracking. Although there are well established quantitative methods in robotics and neighboring fields for addressing these problems, they lack advanced knowledge representation and reasoning capacities. Such capabilities are not only useful in dealing with heterogeneous and incomplete information but moreover they allow for a better inclusion of semantic information and more general homecare and patient-related knowledge. We address this problem and investigate how state-of-the-art localization and tracking methods can be combined with Answer Set Programming, as a popular knowledge representation and reasoning formalism. We report upon a case-study and provide a first experimental evaluation of knowledge-based position estimation both in a simulated as well as in a real setting.

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Authors and Affiliations

  1. Digital Enterprise Research Institute, National University of Ireland, Galway (NUIG), IDA Business Park, Lower Dangan, Galway, Ireland

    Alessandra Mileo

  2. Institut für Informatik, Universität Potsdam, August-Bebel-Str. 89, 14439, Potsdam, Germany

    Torsten Schaub

  3. NOMADIS Research Lab, Department of Informatics, Systems and Communication, University of Milan-Bicocca, viale Sarca 336/14, 20126, Milan, Italy

    Davide Merico & Roberto Bisiani

Authors
  1. Alessandra Mileo
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  2. Torsten Schaub
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  3. Davide Merico
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  4. Roberto Bisiani
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Correspondence to Alessandra Mileo.

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Mileo, A., Schaub, T., Merico, D. et al. Knowledge-based multi-criteria optimization to support indoor positioning. Ann Math Artif Intell 62, 345–370 (2011). https://doi.org/10.1007/s10472-011-9241-2

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  • DOI: https://doi.org/10.1007/s10472-011-9241-2

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