Title:
A realistic benchmark for visual indoor place recognition

dc.contributor.author Pronobis, A.
dc.contributor.author Caputo, B.
dc.contributor.author Jensfelt, Patric
dc.contributor.author Christensen, Henrik I.
dc.contributor.corporatename Georgia Institute of Technology. College of Computing
dc.contributor.corporatename IDIAP Research Institute
dc.contributor.corporatename Georgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatename Kungl. Tekniska Högskolan. Centrum för Autonoma System
dc.date.accessioned 2011-03-21T14:43:30Z
dc.date.available 2011-03-21T14:43:30Z
dc.date.issued 2009-08
dc.description (c) 2009 Elsevier B.V. All rights reserved. en_US
dc.description Digital Object Identifier: 10.1016/j.robot.2009.07.025
dc.description.abstract An 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.identifier.citation Pronobis, 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.issn 0921-8890
dc.identifier.uri http://hdl.handle.net/1853/38198
dc.language.iso en_US en_US
dc.publisher Georgia Institute of Technology en_US
dc.publisher.original Elsevier
dc.subject Visual place recognition en_US
dc.subject Robot topological localization en_US
dc.subject Standard robotic benchmark en_US
dc.title A realistic benchmark for visual indoor place recognition en_US
dc.type Text
dc.type.genre Pre-print
dspace.entity.type Publication
local.contributor.author Christensen, Henrik I.
local.contributor.corporatename Institute for Robotics and Intelligent Machines (IRIM)
relation.isAuthorOfPublication afdc727f-2705-4744-945f-e7d414f2212b
relation.isOrgUnitOfPublication 66259949-abfd-45c2-9dcc-5a6f2c013bcf
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