Dr. Patrick Beeson

Factoring the mapping problem: Mobile robot map-building in the Hybrid Spatial Semantic Hierarchy

Patrick Beeson, Joseph Modayil, and Benjamin Kuipers. Factoring the mapping problem: Mobile robot map-building in the Hybrid Spatial Semantic Hierarchy. International Journal of Robotics Research, 2010.
In Press. Request Pre-print
IJRR Online First. Abstract and link to paper

Abstract

We propose a factored approach to mobile robot map-building that handles qualitatively different types of uncertainty by combining the strengths of topological and metrical approaches. Our framework is based on a computational model of the human cognitive map; thus it allows robust navigation and communication within several different spatial ontologies. This paper focuses exclusively on the issue of map-building using the framework.

Our approach factors the mapping problem into natural sub-goals: building a metrical representation for local small-scale spaces; finding a topological map that represents the qualitative structure of large-scale space; and (when necessary) constructing a metrical representation for large-scale space using the skeleton provided by the topological map. We describe how to abstract a symbolic description of the robot's immediate surround from local metrical models, how to combine these local symbolic models in order to build global symbolic models, and how to create a globally consistent metrical map from a topological skeleton by connecting local frames of reference.

@Article{Beeson-ijrr-press,
  author =	 {Patrick Beeson and Joseph Modayil and Benjamin
                  Kuipers},
  title =	 {Factoring the mapping problem: Mobile robot
                  map-building in the {Hybrid Spatial Semantic
                  Hierarchy}},
  journal =	 {International Journal of Robotics Research},
  year =	 2010,
  abstract =	 {We propose a factored approach to mobile robot
                  map-building that handles qualitatively different
                  types of uncertainty by combining the strengths of
                  topological and metrical approaches. Our framework
                  is based on a computational model of the human
                  cognitive map; thus it allows robust navigation and
                  communication within several different spatial
                  ontologies. This paper focuses exclusively on the
                  issue of map-building using the framework. \par Our
                  approach factors the mapping problem into natural
                  sub-goals: building a metrical representation for
                  local small-scale spaces; finding a topological map
                  that represents the qualitative structure of
                  large-scale space; and (when necessary) constructing
                  a metrical representation for large-scale space
                  using the skeleton provided by the topological
                  map. We describe how to abstract a symbolic
                  description of the robot's immediate surround from
                  local metrical models, how to combine these local
                  symbolic models in order to build global symbolic
                  models, and how to create a globally consistent
                  metrical map from a topological skeleton by
                  connecting local frames of reference.},
  wwwnote =	 {In Press. <a
                  href="mailto:pbeeson@cs.utexas.edu?subject=REQUEST:IJRR_preprint">
                  Request Pre-print</a><br>IJRR Online First. <a
                  href="http://ijr.sagepub.com/cgi/content/abstract/0278364909100586v1">
                  Abstract and link to paper</a>},
  bib2html_pubtype ={Journal},
  bib2html_rescat ={Topological/Hybrid Map-Building},
}

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