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Sensor fusion for mobile robot localization using extended Kalman filter, UWB ToF and ArUco markers

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dc.contributor.authorFaria, Sílvia
dc.contributor.authorLima, José
dc.contributor.authorCosta, Paulo Gomes da
dc.date.accessioned2022-04-05T08:33:09Z
dc.date.available2022-04-05T08:33:09Z
dc.date.issued2021
dc.description.abstractThe ability to locate a robot is one of the main features to be truly autonomous. Different methodologies can be used to determine robots location as accurately as possible, however these methodologies present several problems in some circumstances. One of these problems is the existence of uncertainty in the sensing of the robot. To solve this problem, it is necessary to combine the uncertain information correctly. In this way, it is possible to have a system that allows a more robust localization of the robot, more tolerant to failures and disturbances. This paper evaluates an Extended Kalman Filter (EKF) that fuses odometry information with Ultra-WideBand Time-of-Flight (UWB ToF) measurements and camera measurements from the detection of ArUco markers in the environment. The proposed system is validated in a real environment with a differential robot developed for this purpose, and the achieved results are promising.pt_PT
dc.description.sponsorshipThis work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project UIDB/50014/2020.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFaria, Sílvia; Lima, José; Costa, Paulo (2021). Sensor fusion for mobile robot localization using extended Kalman filter, UWB ToF and ArUco markers. In Pereira, Ana I.; Fernandes, Florbela P.; Coelho, João Paulo; Teixeira, João Paulo; Pacheco, Maria F.; Alves, Paulo; Lopes, Rui Pedro (Eds.) Optimization, learning algorithms and applications: first International Conference, OL2A 2021. Cham: Springer Nature. p. 235-250. ISBN 978-3-030-91884-2pt_PT
dc.identifier.doi10.1007/978-3-030-91885-9_17pt_PT
dc.identifier.isbn978-3-030-91884-2
dc.identifier.urihttp://hdl.handle.net/10198/25335
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherSpringer Naturept_PT
dc.relationINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectArUco markerspt_PT
dc.subjectAutonomous mobile robotpt_PT
dc.subjectExtended kalman filterpt_PT
dc.subjectLocalizationpt_PT
dc.subjectUltra-widebandpt_PT
dc.subjectVision based systempt_PT
dc.titleSensor fusion for mobile robot localization using extended Kalman filter, UWB ToF and ArUco markerspt_PT
dc.typeconference paper
dspace.entity.typePublication
oaire.awardNumberUIDB/50014/2020
oaire.awardTitleINESC TEC- Institute for Systems and Computer Engineering, Technology and Science
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50014%2F2020/PT
oaire.citation.conferencePlaceBragançapt_PT
oaire.citation.endPage250pt_PT
oaire.citation.startPage235pt_PT
oaire.citation.titleOptimization, learning algorithms and applications: first International Conference, OL2A 2021pt_PT
oaire.citation.volume1488pt_PT
oaire.fundingStream6817 - DCRRNI ID
person.familyNameLima
person.givenNameJosé
person.identifierR-000-8GD
person.identifier.ciencia-id6016-C902-86A9
person.identifier.orcid0000-0001-7902-1207
person.identifier.ridL-3370-2014
person.identifier.scopus-author-id55851941311
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsrestrictedAccesspt_PT
rcaap.typeconferenceObjectpt_PT
relation.isAuthorOfPublicationd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication.latestForDiscoveryd88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isProjectOfPublication2957d2e8-0cce-46ca-8e0e-d15ccf4f290e
relation.isProjectOfPublication.latestForDiscovery2957d2e8-0cce-46ca-8e0e-d15ccf4f290e

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