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Poster Abstract: A Model For Semantic Localization
Matt Weber, Edward A. Lee

Citation
Matt Weber, Edward A. Lee. "Poster Abstract: A Model For Semantic Localization". Talk or presentation, April, 2015.

Abstract
We propose a model for Semantic Localization, i.e. establishing positional relations on meaningful objects, to enable the principled integration of heterogenous localization clues { such as those derived from ubiquitous sensors in the Internet of Things. Our approach is two-pronged: we consider relation-structured Phenomenal Maps alongside spatially-organized Physical Maps. Phenomenal Maps may be used to answer semantic queries about the relative position of objects without necessarily resorting to physical coordinates. Physical Maps are not restricted to purely Euclidian spaces, to the contrary we identify useful applications for topological, and metrical maps among others. We give the framework for a structured mechanism through which localization information in all these representations may be reconciled.

Electronic downloads

Citation formats  
  • HTML
    Matt Weber, Edward A. Lee. <a
    href="http://chess.eecs.berkeley.edu/pubs/1094.html"
    ><i>Poster Abstract: A Model For Semantic
    Localization</i></a>, Talk or presentation, 
    April, 2015.
  • Plain text
    Matt Weber, Edward A. Lee. "Poster Abstract: A Model
    For Semantic Localization". Talk or presentation, 
    April, 2015.
  • BibTeX
    @presentation{WeberLee15_PosterAbstractModelForSemanticLocalization,
        author = {Matt Weber and Edward A. Lee},
        title = {Poster Abstract: A Model For Semantic Localization},
        month = {April},
        year = {2015},
        abstract = {We propose a model for Semantic Localization, i.e.
                  establishing positional relations on meaningful
                  objects, to enable the principled integration of
                  heterogenous localization clues { such as those
                  derived from ubiquitous sensors in the Internet of
                  Things. Our approach is two-pronged: we consider
                  relation-structured Phenomenal Maps alongside
                  spatially-organized Physical Maps. Phenomenal Maps
                  may be used to answer semantic queries about the
                  relative position of objects without necessarily
                  resorting to physical coordinates. Physical Maps
                  are not restricted to purely Euclidian spaces, to
                  the contrary we identify useful applications for
                  topological, and metrical maps among others. We
                  give the framework for a structured mechanism
                  through which localization information in all
                  these representations may be reconciled.},
        URL = {http://chess.eecs.berkeley.edu/pubs/1094.html}
    }
    

Posted by Mary Stewart on 9 Mar 2015.
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