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Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments

2024-10-17Artigo científico Acesso aberto
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dc.contributor.authorTonini, Andrea
dc.contributor.authorCastelli, Mauro
dc.contributor.authorBates, Jordan Steven
dc.contributor.authorLin, Nyi Nyi Nyan
dc.contributor.authorPainho, Marco
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.institutionNOVA Information Management School (NOVA IMS)
dc.contributor.pblMDPI - Multidisciplinary Digital Publishing Institute
dc.date.accessioned2024-10-24T23:20:09Z
dc.date.available2024-10-24T23:20:09Z
dc.date.issued2024-10-17
dc.descriptionTonini, A., Castelli, M., Bates, J. S., Lin, N. N. N., & Painho, M. (2024). Visual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environments. Applied Sciences, 14(20), 1-13. Article 9493. https://doi.org/10.3390/app14209493 --- This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia) under the project UIDB/04152/2020 (DOI: 10.54499/UIDB/04152/2020)—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. This work was partially funded by the Deutsche Forschungsgemeinschaft under Germany’s Excellence Strategy. The authors would like to express their gratitude to the NOVA Impact Office (https://novainnovation.unl.pt/nova-impact-office/, accessed on 10 February 2024) for all the support provided.
dc.description.abstractEstimating the location of unmanned aerial vehicles (UAVs) within a global coordinate system can be achieved by correlating known world points with their corresponding image projections captured by the vehicle’s camera. Reducing the number of required world points may lower the computational requirements needed for such estimation. This paper introduces a novel method for determining the absolute position of aerial vehicles using only two known coordinate points that reduce the calculation complexity and, therefore, the computation time. The essential parameters for this calculation include the camera’s focal length, detector dimensions, and the Euler angles for Pitch and Roll. The Yaw angle is not required, which is beneficial because Yaw is more susceptible to inaccuracies due to environmental factors. The vehicle’s position is determined through a sequence of straightforward rigid transformations, eliminating the need for additional points or iterative processes for verification. The proposed method was tested using a Digital Elevation Model (DEM) created via LiDAR and 11 aerial images captured by a UAV. The results were compared against Global Navigation Satellite Systems (GNSSs) data and other common image pose estimation methodologies. While the available data did not permit precise error quantification, the method demonstrated performance comparable to GNSS-based approaches.en
dc.description.versionpublishersversion
dc.description.versionpublished
dc.format.extent13
dc.format.extent626532
dc.identifier.doi10.3390/app14209493
dc.identifier.issn2076-3417
dc.identifier.otherPURE: 101455505
dc.identifier.otherPURE UUID: 53f5d598-70be-4d37-9449-89d72ef1a8c9
dc.identifier.otherScopus: 85207440600
dc.identifier.otherWOS: 001341381900001
dc.identifier.otherORCID: /0000-0003-1136-3387/work/170276122
dc.identifier.otherORCID: /0000-0002-8793-1451/work/170276213
dc.identifier.urihttp://hdl.handle.net/10362/174020
dc.identifier.urlhttps://www.scopus.com/pages/publications/85207440600
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001341381900001
dc.language.isoeng
dc.peerreviewedyes
dc.relationinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
dc.relationInformation Management Research Center
dc.subjectGNNS-denied environments
dc.subjectlocalization of aerial vehicles
dc.subjectvisual-inertial method
dc.subjectGeneral Materials Science
dc.subjectInstrumentation
dc.subjectGeneral Engineering
dc.subjectProcess Chemistry and Technology
dc.subjectComputer Science Applications
dc.subjectFluid Flow and Transfer Processes
dc.subjectSDG 9 - Industry, Innovation, and Infrastructure
dc.subjectSDG 11 - Sustainable Cities and Communities
dc.titleVisual-Inertial Method for Localizing Aerial Vehicles in GNSS-Denied Environmentsen
dc.typejournal article
degois.publication.firstPage1
degois.publication.issue20
degois.publication.lastPage13
degois.publication.titleApplied Sciences
degois.publication.volume14
dspace.entity.typePublication
oaire.awardNumberUIDB/04152/2020
oaire.awardTitleInformation Management Research Center
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04152%2F2020/PT
oaire.fundingStream6817 - DCRRNI ID
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccess
relation.isProjectOfPublication3274bdb3-4dd3-4bbe-8f74-d34190081f87
relation.isProjectOfPublication.latestForDiscovery3274bdb3-4dd3-4bbe-8f74-d34190081f87

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