Using the topological skeleton for scalable, global, metrical map-building

Joseph Modayil, Patrick Beeson, and Benjamin Kuipers. Using the topological skeleton for scalable, global, metrical map-building. In Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), pp. 1530–1536, Sendai, Japan, September 2004.

Abstract

Most simultaneous localization and mapping (SLAM) approaches focus on purely metrical approaches to map-building. We present a method for computing the global metrical map that builds on the structure provided by the topological map. This allows us to factor the uncertainty in the map into local metrical uncertainty (which is handled well by existing SLAM methods), global topological uncertainty (which is handled well by existing topological map-learning methods), and global metrical uncertainty (which can be handled effectively once the other types of uncertainty are factored out). We believe that this method for building the global metrical map will be scalable to very large environments.

BibTeX

@InProceedings{Modayil-iros-04,
  author =       {Joseph Modayil and Patrick Beeson and Benjamin Kuipers},
  title =        {Using the topological skeleton for scalable, global,
                  metrical map-building},
  booktitle =    {Proceedings of the IEEE/RSJ Conference on Intelligent Robots
                  and Systems (IROS)},
  year =         2004,
  address =      {Sendai, Japan},
  month =        {September},
  pages =        {1530--1536},
  abstract =     {Most simultaneous localization and mapping (SLAM) approaches
                  focus on purely metrical approaches to map-building. We
                  present a method for computing the global metrical map that
                  builds on the structure provided by the topological
                  map. This allows us to factor the uncertainty in the map
                  into local metrical uncertainty (which is handled well by
                  existing SLAM methods), global topological uncertainty
                  (which is handled well by existing topological map-learning
                  methods), and global metrical uncertainty (which can be
                  handled effectively once the other types of uncertainty are
                  factored out). We believe that this method for building the
                  global metrical map will be scalable to very large
                  environments.},
  bib2html_pubtype ={Refereed Conference},
  bib2html_rescat ={Simultaneous Localization and Mapping (SLAM)},
}

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