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dc.contributor.authorBriales Garcia, Jesus
dc.contributor.authorGonzález-Jiménez, Antonio Javier 
dc.date.accessioned2017-09-12T11:29:03Z
dc.date.available2017-09-12T11:29:03Z
dc.date.issued2017-05
dc.identifier.citationInt. Conf. on Robotics and Automation (ICRA).es_ES
dc.identifier.urihttp://hdl.handle.net/10630/14454
dc.description.abstractPose Graph Optimization (PGO) is the de facto choice to solve the trajectory of an agent in Simultaneous Localization and Mapping (SLAM). The Maximum Likelihood Estimation (MLE) for PGO is a non-convex problem for which no known technique is able to guarantee a globally optimal solution under general conditions. In recent years, Lagrangian duality has proved suitable to provide good, frequently tight relaxations of the hard PGO problem through convex Semidefinite Programming (SDP). In this work, we build from the state-of-the-art Lagrangian relaxation [1] and contribute a complete recovery procedure that, given the (tractable) optimal solution of the relaxation, provides either the optimal MLE solution if the relaxation is tight, or a remarkably good feasible guess if the relaxation is non-tight, which occurs in specially challenging PGO problems (very noisy observations, low graph connectivity, etc.). In the latter case, when used for initialization of local iterative methods, our approach outperforms other state-ofthe- art approaches converging to better solutions. We support our claims with extensive experiments.es_ES
dc.description.sponsorshipUniversity of Malaga travel grant, the Spanish grant program FPU14/06098 and the project PROMOVE (DPI2014-55826-R), funded by the Spanish Government and the "European Regional Development Fund". Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.es_ES
dc.language.isoenges_ES
dc.publisherIEEEes_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.subjectProgramación (Matemáticas)es_ES
dc.subject.otherPose Graphes_ES
dc.subject.otherSLAMes_ES
dc.subject.otherLagrangian dualityes_ES
dc.subject.otherSemidefinite Programminges_ES
dc.titleInitialization of 3D Pose Graph Optimization using Lagrangian dualityes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.centroE.T.S.I. Informáticaes_ES
dc.relation.eventtitleInt. Conf. on Robotics and Automation (ICRA)es_ES
dc.relation.eventplaceSingaporees_ES
dc.relation.eventdateMay, 2017es_ES
dc.identifier.orcidhttp://orcid.org/0000-0003-3845-3497es_ES
dc.cclicenseby-nc-ndes_ES


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