PL-SLAM: real-time monocular visual SLAM with points and lines

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dc.contributor	Institut de Robòtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor	Universitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.contributor	Universitat Politècnica de Catalunya. VIS - Visió Artificial i Sistemes Intel.ligents
dc.contributor.author	Pumarola Peris, Albert
dc.contributor.author	Vakhitov, A.
dc.contributor.author	Agudo Martínez, Antonio
dc.contributor.author	Sanfeliu Cortés, Alberto
dc.contributor.author	Moreno-Noguer, Francesc
dc.date	2017
dc.identifier.citation	Pumarola, A., Vakhitov, A., Agudo, A., Sanfeliu, A., Moreno-Noguer, F. PL-SLAM: real-time monocular visual SLAM with points and lines. A: IEEE International Conference on Robotics and Automation. "2017 ICRA IEEE International Conference on Robotics and Automation". IEEE Press, 2017, p. 4503-4508.
dc.identifier.citation	10.1109/ICRA.2017.7989522
dc.identifier.uri	http://hdl.handle.net/2117/110259
dc.language.iso	eng
dc.publisher	IEEE Press
dc.relation	http://ieeexplore.ieee.org/document/7989522/
dc.relation	info:eu-repo/grantAgreement/EC/H2020/644271/EU/AErial RObotic system integrating multiple ARMS and advanced manipulation capabilities for inspection and maintenance/AEROARMS
dc.rights	Attribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject	Àrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subject	computer vision
dc.subject	feature extraction
dc.subject	mobile robots
dc.subject	SLAM
dc.subject	Classificació INSPEC::Pattern recognition::Computer vision
dc.title	PL-SLAM: real-time monocular visual SLAM with points and lines
dc.type	info:eu-repo/semantics/submittedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, 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 component of this work in other works
dc.description.abstract	Low textured scenes are well known to be one of the main Achilles heels of geometric computer vision algorithms relying on point correspondences, and in particular for visual SLAM. Yet, there are many environments in which, despite being low textured, one can still reliably estimate line-based geometric primitives, for instance in city and indoor scenes, or in the so-called
dc.description.abstract	Peer Reviewed

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