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dc.contributor.authorAlcantarilla, Pablo F.
dc.contributor.authorOh, Sang Min
dc.contributor.authorMariottini, Gian Luca
dc.contributor.authorBergasa, Luis M.
dc.contributor.authorDellaert, Frank
dc.date.accessioned2011-03-29T17:19:33Z
dc.date.available2011-03-29T17:19:33Z
dc.date.issued2010
dc.identifier.citationAlcantarilla, P.F., Oh, S. M., Mariottini, G.L., Bergasa, L.M., Dellaert, F.(2010). “Learning Visibility of Landmarks for Vision-Based Localization”. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2010), 3-7 May 2010, 4881-4888.en_US
dc.identifier.issn1050-4729
dc.identifier.urihttp://hdl.handle.net/1853/38323
dc.description©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.en_US
dc.descriptionPresented at the 2010 IEEE International Conference on Robotics and Automation (ICRA), 3-7 May 2010, Anchorage, AK.
dc.descriptionDOI: 10.1109/ROBOT.2010.5509383
dc.description.abstractWe aim to perform robust and fast vision-based localization using a pre-existing large map of the scene. A key step in localization is associating the features extracted from the image with the map elements at the current location. Although the problem of data association has greatly benefited from recent advances in appearance-based matching methods, less attention has been paid to the effective use of the geometric relations between the 3D map and the camera in the matching process. In this paper we propose to exploit the geometric relationship between the 3D map and the camera pose to determine the visibility of the features. In our approach, we model the visibility of every map feature w.r.t. the camera pose using a non-parametric distribution model. We learn these non-parametric distributions during the 3D reconstruction process, and develop efficient algorithms to predict the visibility of features during localization. With this approach, the matching process only uses those map features with the highest visibility score, yielding a much faster algorithm and superior localization results. We demonstrate an integrated system based on the proposed idea and highlight its potential benefits for the localization in large and cluttered environments.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectCamera poseen_US
dc.subjectData associationen_US
dc.subjectLocalizationen_US
dc.subject3D landmarken_US
dc.subject3D mapsen_US
dc.subjectVisibilityen_US
dc.titleLearning Visibility of Landmarks for Vision-Based Localizationen_US
dc.typePost-printen_US
dc.typeProceedings
dc.contributor.corporatenameGeorgia Institute of Technology. Center for Robotics and Intelligent Machines
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computing
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computing
dc.contributor.corporatenameUniversidad de Alcalá. Departamento de Electrónica
dc.contributor.corporatenameUniversity of Minnesota. Dept. of Computer Science and Engineering
dc.publisher.originalInstitute of Electrical and Electronics Engineers


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