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dc.contributor.authorSchmidt, J
dc.contributor.authorWong, CK
dc.contributor.authorYeap, WK
dc.date.accessioned2011-12-21T03:10:22Z
dc.date.available2011-12-21T03:10:22Z
dc.date.copyright2006
dc.date.issued2011-12-21
dc.identifier.citationThird International Conference on Autonomous Robots and Agents, Palmerston North, New Zealand, pages 497 - 502
dc.identifier.urihttp://hdl.handle.net/10292/3229
dc.description.abstractWe present an approach for indoor mapping and localization with a mobile robot using sparse range data, without the need for solving the SLAM problem. The paper consists of two main parts. First, a split and merge based method for dividing a given metric map into distinct regions is presented, thus creating a topological map in a metric framework. Spatial information extracted from this map is then used for self-localization. The robot computes local confidence maps for two simple localization strategies based on distance and relative orientation of regions. The local confidence maps are then fused using an approach adapted from computer vision to produce overall confidence maps. Experiments on data acquired by mobile robots equipped with sonar sensors are presented.
dc.publisherAUT University
dc.relation.urihttp://www-ist.massey.ac.nz/conferences/icara2006a/files/Prog_FINAL.pdf#wed
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in (see Citation). The original publication is available at (see Publisher's Version)
dc.subjectMobile Robots, Mapping, Localization
dc.titleMapping and localisation with sparse range data
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess


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