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dc.contributor.authorBlanco, José Luis
dc.contributor.authorMañas, Francisco
dc.contributor.authorTorres-Moreno, José Luis
dc.contributor.authorRodríguez, Francisco
dc.contributor.authorGiménez Fernández, Antonio
dc.date.accessioned2019-07-17T17:26:15Z
dc.date.available2019-07-17T17:26:15Z
dc.date.issued2019-07
dc.identifier.urihttp://hdl.handle.net/10835/6915
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/m2 is required to achieve 100% convergence success for large-scale (100,000 m2), 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.relationinfo:eu-repo/grantAgreement/ES/MINECOinfo:eu-repo/grantAgreement/ES/MINECO/DPI2017-85007-R/ES/Control y gestión óptima de recursos heterogéneos en distritos productivos agroindustriales integrando energías renovables/CHROMAEes_ES
dc.titleBenchmarking Particle Filter Algorithms for Efficient Velodyne-Based Vehicle Localizationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.relation.projectIDMINECO/DPI2017-85007-Res_ES


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