Publication:
A novel online approach for drift covariance estimation of odometries used in intelligent vehicle localization

dc.affiliation.dptoUC3M. Departamento de Ingeniería de Sistemas y Automáticaes
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Laboratorio de Sistemas Inteligenteses
dc.contributor.authorOsman, Mostafa
dc.contributor.authorHussein, Ahmed Ali Mahmoud
dc.contributor.authorAl Kaff, Abdulla Hussein Abdulrahman
dc.contributor.authorGarcía Fernández, Fernando
dc.contributor.authorCao, Dongpu
dc.contributor.funderMinisterio de Economía y Competitividad (España)es
dc.date.accessioned2020-04-17T10:03:23Z
dc.date.available2020-04-17T10:03:23Z
dc.date.issued2019-11-26
dc.descriptionOsman, M., Hussein, A., Al-Kaff, A., Fernández, F. G., & Cao, D. (2020). Correction: Osman, M., et al. A Novel Online Approach for Drift Covariance Estimation of Odometries Used in Intelligent Vehicle Localization. Sensors 2019, 19, 5178. Sensors, 20(4), 1162. https://doi.org/10.3390/s20041162en
dc.description.abstractLocalization is the fundamental problem of intelligent vehicles. For a vehicle to autonomously operate, it first needs to locate itself in the environment. A lot of different odometries (visual, inertial, wheel encoders) have been introduced through the past few years for autonomous vehicle localization. However, such odometries suffers from drift due to their reliance on integration of sensor measurements. In this paper, the drift error in an odometry is modeled and a Drift Covariance Estimation (DCE) algorithm is introduced. The DCE algorithm estimates the covariance of an odometry using the readings of another on-board sensor which does not suffer from drift. To validate the proposed algorithm, several real-world experiments in different conditions as well as sequences from Oxford RobotCar Dataset and EU long-term driving dataset are used. The effect of the covariance estimation on three different fusion-based localization algorithms (EKF, UKF and EH-infinity) is studied in comparison with the use of constant covariance, which were calculated based on the true variance of the sensors being used. The obtained results show the efficacy of the estimation algorithm compared to constant covariances in terms of improving the accuracy of localization.en
dc.description.sponsorshipThis research was supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2016-78886-C3-1-R).en
dc.format.extent26es
dc.identifier.bibliographicCitationSensors 2019, 19(23), 5178, Pp. 1-26en
dc.identifier.doihttps://doi.org/10.3390/s19235178
dc.identifier.issn1424-8220
dc.identifier.publicationfirstpage1es
dc.identifier.publicationissue23 (5178)es
dc.identifier.publicationlastpage26es
dc.identifier.publicationtitleSENSORSen
dc.identifier.publicationvolume19es
dc.identifier.urihttps://hdl.handle.net/10016/30139
dc.identifier.uxxiAR/0000024359
dc.language.isoengen
dc.publisherMDPIes
dc.relation.ispartofhttps://doi.org/10.3390/s20041162
dc.relation.projectIDGobierno de España. TRA2015-63708-Res
dc.relation.projectIDGobierno de España. TRA2016-78886-C3-1-Res
dc.rights© 2019 by the authors. Licensee MDPI, Basel, Switzerland.en
dc.rightsThis article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY)en
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.accessRightsopen accessen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.ecienciaRobótica e Informática Industriales
dc.subject.otherAdaptive filteringen
dc.subject.otherCovariance estimationen
dc.subject.otherIntelligent vehiclesen
dc.subject.otherLocalizationen
dc.subject.otherOdometries drift errorsen
dc.subject.otherRos-baseden
dc.titleA novel online approach for drift covariance estimation of odometries used in intelligent vehicle localizationen
dc.typeresearch article*
dc.type.hasVersionVoR*
dspace.entity.typePublication
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