Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10362/151629 |
Resumo: | Pessanha Santos, N., Lobo, V., & Bernardino, A. (2023). Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing. Drones, 7(4), 243. MDPI AG. Retrieved from http://dx.doi.org/10.3390/drones7040243 |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landingalgorithm design and analysiscomputer visionunmanned aerial vehiclemodel-based trackingmotion estimationdirectional statisticsautonomous landingControl and Systems EngineeringInformation SystemsAerospace EngineeringComputer Science ApplicationsArtificial IntelligencePessanha Santos, N., Lobo, V., & Bernardino, A. (2023). Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing. Drones, 7(4), 243. MDPI AG. Retrieved from http://dx.doi.org/10.3390/drones7040243The vast increase in the available computational capability has allowed the application of Particle-Filter (PF)-based approaches for monocular 3D-model-based tracking. These filters depend on the computation of a likelihood function that is usually unavailable and can be approximated using a similarity metric. We can use temporal filtering techniques between filter iterations to achieve better results when dealing with this suboptimal approximation, which is particularly important when dealing with the Unmanned Aerial Vehicle (UAV) model symmetry. The similarity metric evaluation time is another critical concern since we usually want a real-time implementation. We explored, tested, and compared with the same dataset two different types of PFs, (i) an Unscented Bingham Filter (UBiF) and (ii) an Unscented Bingham–Gauss Filter (UBiGaF), using pose optimization in both implementations. Using optimization steps between iterations increases the convergence capability of the filter and decreases the obtained error. A new tree-based similarity metric approach is also explored based on the Distance Transform (DT), allowing a faster evaluation of the possibilities without losing accuracy. The results showed that the obtained pose estimation error is compatible with the automatic landing requirements.Information Management Research Center (MagIC) - NOVA Information Management SchoolNOVA Information Management School (NOVA IMS)RUNLobo, VictorSantos, Nuno PessanhaBernardino, Alexandre2023-04-05T22:18:22Z2023-04-012023-04-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article25application/pdfhttp://hdl.handle.net/10362/151629eng2504-446XPURE: 57728636https://doi.org/10.3390/drones7040243info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:34:05Zoai:run.unl.pt:10362/151629Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:40.174125Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
title |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
spellingShingle |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing Lobo, Victor algorithm design and analysis computer vision unmanned aerial vehicle model-based tracking motion estimation directional statistics autonomous landing Control and Systems Engineering Information Systems Aerospace Engineering Computer Science Applications Artificial Intelligence |
title_short |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
title_full |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
title_fullStr |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
title_full_unstemmed |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
title_sort |
Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing |
author |
Lobo, Victor |
author_facet |
Lobo, Victor Santos, Nuno Pessanha Bernardino, Alexandre |
author_role |
author |
author2 |
Santos, Nuno Pessanha Bernardino, Alexandre |
author2_role |
author author |
dc.contributor.none.fl_str_mv |
Information Management Research Center (MagIC) - NOVA Information Management School NOVA Information Management School (NOVA IMS) RUN |
dc.contributor.author.fl_str_mv |
Lobo, Victor Santos, Nuno Pessanha Bernardino, Alexandre |
dc.subject.por.fl_str_mv |
algorithm design and analysis computer vision unmanned aerial vehicle model-based tracking motion estimation directional statistics autonomous landing Control and Systems Engineering Information Systems Aerospace Engineering Computer Science Applications Artificial Intelligence |
topic |
algorithm design and analysis computer vision unmanned aerial vehicle model-based tracking motion estimation directional statistics autonomous landing Control and Systems Engineering Information Systems Aerospace Engineering Computer Science Applications Artificial Intelligence |
description |
Pessanha Santos, N., Lobo, V., & Bernardino, A. (2023). Fixed-Wing Unmanned Aerial Vehicle 3D-Model-Based Tracking for Autonomous Landing. Drones, 7(4), 243. MDPI AG. Retrieved from http://dx.doi.org/10.3390/drones7040243 |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-04-05T22:18:22Z 2023-04-01 2023-04-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10362/151629 |
url |
http://hdl.handle.net/10362/151629 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
2504-446X PURE: 57728636 https://doi.org/10.3390/drones7040243 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
25 application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799138135297228800 |