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dc.contributor.authorRogers, John G.en_US
dc.contributor.authorTrevor, Alexander J.en_US
dc.contributor.authorNieto-Granda, Carlosen_US
dc.contributor.authorChristensen, Henrik I.en_US
dc.date.accessioned2013-04-26T19:59:14Z
dc.date.available2013-04-26T19:59:14Z
dc.date.issued2011-09
dc.identifier.citationJohn Rogers III, Alexander J. Trevor, Carlos Nieto-Granda, and Henrik Iskov Christensen, “Simultaneous localization and mapping with learned object recognition and semantic data association," 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1264-1270, September 2011.en_US
dc.identifier.isbn978-1-61284-454-1
dc.identifier.issn2153-0858
dc.identifier.urihttp://hdl.handle.net/1853/46852
dc.description© 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holderen_US
dc.descriptionPresented at IROS 2011, IEEE/RSJ International Conference on Intelligent Robots and Systems, September 25-30, 2011, San Francisco, CA, USA.en_US
dc.descriptionDOI: 10.1109/IROS.2011.6095152en_US
dc.description.abstractComplex and structured landmarks like objects have many advantages over low-level image features for semantic mapping. Low level features such as image corners suffer from occlusion boundaries, ambiguous data association, imaging artifacts, and viewpoint dependance. Artificial landmarks are an unsatisfactory alternative because they must be placed in the environment solely for the robot's benefit. Human environments contain many objects which can serve as suitable landmarks for robot navigation such as signs, objects, and furniture. Maps based on high level features which are identified by a learned classifier could better inform tasks such as semantic mapping and mobile manipulation. In this paper we present a technique for recognizing door signs using a learned classifier as one example of this approach, and demonstrate their use in a graphical SLAM framework with data association provided by reasoning about the semantic meaning of the sign.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectSemantic mappingen_US
dc.subjectSLAMen_US
dc.subjectSimultaneous localization and mappingen_US
dc.subjectLearned object recognitionen_US
dc.titleSimultaneous Localization and Mapping with Learned Object Recognition and Semantic Data Associationen_US
dc.typeProceedingsen_US
dc.typePost-printen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machinesen_US
dc.publisher.originalInstitute of Electrical and Electronics Engineersen_US
dc.identifier.doi10.1109/IROS.2011.6095152


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