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, 29(4):428–459, April 2010.
Local download is a pre-print version. Final version can be found here.
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.
Additional Information
Local download is a pre-print version. Final version can be found here.
BibTeX
@Article{Beeson-ijrr-10,
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,
volume = 29,
number = 4,
month = April,
pages = {428--459},
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.},
url = {http://dx.doi.org/10.1177/0278364909100586},
wwwnote = {Local download is a pre-print version. Final
version can be found <a
href="http://dx.doi.org/10.1177/0278364909100586">
here</a>.},
bib2html_extra_info ={Local download is a pre-print version. Final
version can be found <a
href="http://dx.doi.org/10.1177/0278364909100586">
here</a>.},
bib2html_pubtype ={Journal},
bib2html_rescat ={Topological/Hybrid Map-Building},
}