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dc.contributor.authorBlanco Claraco, José Luis
dc.contributor.authorMañas Alvarez, Francisco
dc.contributor.authorTorres Moreno, José Luis
dc.contributor.authorRodríguez Díaz, Francisco
dc.contributor.authorGiménez Fernández, Antonio
dc.date.accessioned2020-01-17T12:46:20Z
dc.date.available2020-01-17T12:46:20Z
dc.date.issued2019-07-17
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/10835/7556
dc.description.abstractKeeping a vehicle well-localized within a prebuilt-map is at the core of any autonomous vehicle navigation system. In this work, we show that both standard SIR sampling and rejection-based optimal sampling are suitable for efficient (10 to 20 ms) real-time pose tracking without feature detection that is using raw point clouds from a 3D LiDAR. Motivated by the large amount of information captured by these sensors, we perform a systematic statistical analysis of how many points are actually required to reach an optimal ratio between efficiency and positioning accuracy. Furthermore, initialization from adverse conditions, e.g., poor GPS signal in urban canyons, we also identify the optimal particle filter settings required to ensure convergence. Our findings include that a decimation factor between 100 and 200 on incoming point clouds provides a large savings in computational cost with a negligible loss in localization accuracy for a VLP-16 scanner. Furthermore, an initial density of ∼2 particles/m 2 is required to achieve 100% convergence success for large-scale (∼100,000 m 2 ), outdoor global localization without any additional hint from GPS or magnetic field sensors. All implementations have been released as open-source software.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectglobal positioning systemes_ES
dc.subjectmobile robotses_ES
dc.subjectsimultaneous localization and mappinges_ES
dc.subjectparticle filteres_ES
dc.subjectdistrictes_ES
dc.titleBenchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/19/14/3155es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional