Exploiting local perceptual models for topological map-building

Patrick Beeson, Matt MacMahon, Joseph Modayil, Jefferson Provost, Francesco Savelli, and Benjamin Kuipers. Exploiting local perceptual models for topological map-building. In IJCAI Workshop on Reasoning with Uncertainty in Robotics (RUR), pp. 15–22, Acapulco, Mexico, August 2003.

Abstract

The Spatial Semantic Hierarchy (SSH) provides a robot-independent ontology and logical theory for building topological maps of large-scale environments online. Existing SSH implementations make very limited use of perceptual information and thus create many candidate maps. Metrical mapping implementations capture detailed knowledge about local small-scale space but do not handle large environments well due to computational limitations and global metrical uncertainty. In this paper, we extend the SSH to utilize better sensory information by incorporating information derived from local metrical models into the large-scale space framework. This new extension of the Spatial Semantic Hierarchy uses local topology obtained from local perceptual models to constrain a global topological map search.

Additional Information

Also see: Benjamin Kuipers, Joseph Modayil, Patrick Beeson, Matt MacMahon, and Francesco Savelli. Local metrical and global topological maps in the Hybrid Spatial Semantic Hierarchy. IEEE International Conference on Robotics and Automation (ICRA), 2004.

BibTeX

@InProceedings{Beeson-rur-03,
  author =       {Patrick Beeson and Matt MacMahon and Joseph Modayil and
                  Jefferson Provost and Francesco Savelli and Benjamin
                  Kuipers},
  title =        {Exploiting local perceptual models for topological
                  map-building},
  booktitle =    {IJCAI Workshop on Reasoning with Uncertainty in Robotics
                  (RUR)},
  year =         2003,
  address =      {Acapulco, Mexico},
  month =        {August},
  pages =        {15--22},
  abstract =     {The Spatial Semantic Hierarchy (SSH) provides a
                  robot-independent ontology and logical theory for building
                  topological maps of large-scale environments
                  online. Existing SSH implementations make very limited use
                  of perceptual information and thus create many candidate
                  maps. Metrical mapping implementations capture detailed
                  knowledge about local small-scale space but do not handle
                  large environments well due to computational limitations and
                  global metrical uncertainty. In this paper, we extend the
                  SSH to utilize better sensory information by incorporating
                  information derived from local metrical models into the
                  large-scale space framework. This new extension of the
                  Spatial Semantic Hierarchy uses local topology obtained from
                  local perceptual models to constrain a global topological
                  map search.},
  bib2html_pubtype ={Workshop},
  bib2html_rescat ={Topological/Hybrid Map-Building},
  bib2html_extra_info ={Also see: Benjamin Kuipers, Joseph Modayil, Patrick
                  Beeson, Matt MacMahon, and Francesco Savelli. <a
                  href="http://personal.traclabs.com/~pbeeson/publications/b2hd-Kuipers-icra-04.html">
                  Local metrical and global topological maps in the Hybrid
                  Spatial Semantic Hierarchy.</a> IEEE International
                  Conference on Robotics and Automation (ICRA), 2004.}
}

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