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dc.contributor.authorPronobis, A.
dc.contributor.authorCaputo, B.
dc.contributor.authorJensfelt, Patric
dc.contributor.authorChristensen, Henrik I.
dc.date.accessioned2011-03-21T14:43:30Z
dc.date.available2011-03-21T14:43:30Z
dc.date.issued2009-08
dc.identifier.citationPronobis, A., Caputo, B., Jensfelt, P., and Christensen, H. I. A realistic benchmark for visual indoor place recognition. Robotics and Autonomous Systems (Aug 2009).en_US
dc.identifier.issn0921-8890
dc.identifier.urihttp://hdl.handle.net/1853/38198
dc.description(c) 2009 Elsevier B.V. All rights reserved.en_US
dc.descriptionDigital Object Identifier: 10.1016/j.robot.2009.07.025
dc.description.abstractAn important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Recent advances in vision have made this modality a viable alternative to the traditional range sensors and visual place recognition algorithms emerged as a useful and widely applied tool for obtaining information about robot’s position. Several place recognition methods have been proposed using vision alone or combined with sonar and/or laser. This research calls for standard benchmark datasets for development, evaluation and comparison of solutions. To this end, this paper presents two carefully designed and annotated image databases augmented with an experimental procedure and extensive baseline evaluation. The databases were gathered in an uncontrolled indoor office environment using two mobile robots and a standard camera. The acquisition spanned across a time range of several months and different illumination and weather conditions. Thus, the databases are very well suited for evaluating the robustness of algorithms with respect to a broad range of variations, often occurring in real-world settings. We thoroughly assessed the databases with a purely appearance-based place recognition method based on Support Vector Machines and two types of rich visual features (global and local).en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectVisual place recognitionen_US
dc.subjectRobot topological localizationen_US
dc.subjectStandard robotic benchmarken_US
dc.titleA realistic benchmark for visual indoor place recognitionen_US
dc.typePre-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameIDIAP Research Institute
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameKungl. Tekniska Högskolan. Centrum för Autonoma System
dc.publisher.originalElsevier


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