MétaCan
Menu
Retour à la cohorte
Enregistrement W3164830547 · doi:10.2514/1.i010975

Vision-Based Pose Estimation of Fixed-Wing Aircraft Using You Only Look Once and Perspective-n-Points

2021· article· en· W3164830547 sur OpenAlexaboutno aff
Sukkeun Kim, Jeongho Kim, Ji-Hoon Park, Daewoo Lee

Notice bibliographique

RevueJournal of Aerospace Information Systems · 2021
Typearticle
Langueen
DomaineEngineering
ThématiqueRobotics and Sensor-Based Localization
Établissements canadiensnon disponible
Organismes subventionnairesKorea Evaluation Institute of Industrial Technology
Mots-clésFixed wingPerspective (graphical)PoseComputer visionComputer scienceEstimationArtificial intelligenceWingEngineeringAerospace engineering

Résumé

récupéré en direct d'OpenAlex

No AccessTechnical NotesVision-Based Pose Estimation of Fixed-Wing Aircraft Using You Only Look Once and Perspective-n-PointsSukkeun Kim, Jeongho Kim, Jihoon Park and Daewoo LeeSukkeun Kim https://orcid.org/0000-0001-6903-5437Pusan National University, Busan 46241, Republic of Korea, Jeongho KimNextfoam, Seoul 08512, Republic of Korea, Jihoon ParkPusan National University, Busan 46241, Republic of Korea and Daewoo Lee https://orcid.org/0000-0002-9546-0610Pusan National University, Busan 46241, Republic of KoreaPublished Online:21 May 2021https://doi.org/10.2514/1.I010975SectionsRead Now ToolsAdd to favoritesDownload citationTrack citations ShareShare onFacebookTwitterLinked InRedditEmail About References [1] Vetrella A., Fasano G. and Accardo D., "Attitude Estimation for Cooperating UAVs Based on Tight Integration of GNSS and Vision Measurements," Aerospace Science and Technology, Vol. 84, Jan. 2019, pp. 966–979. https://doi.org/10.1016/j.ast.2018.11.032 CrossrefGoogle Scholar[2] Pesce V., Opromolla R., Sarno S., Lavagna M. and Grassi M., "Autonomous Relative Navigation Around Uncooperative Spacecraft Based on a Single Camera," Aerospace Science and Technology, Vol. 84, Jan. 2019, pp. 1070–1080. https://doi.org/10.1016/j.ast.2018.11.042 CrossrefGoogle Scholar[3] Watanabe Y., Calise A. and Johnson E., "Vision-Based Obstacle Avoidance for UAVs," Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA Paper 2007-6829, 2007. https://doi.org/10.2514/6.2007-6829 LinkGoogle Scholar[4] Chatterji G. B., Menon P. K. and Sridhar B., "GPS/Machine Vision Navigation System for Aircraft," IEEE Transactions on Aerospace and Electronic Systems, Vol. 33, No. 3, 1997, pp. 1012–1025. https://doi.org/10.1109/7.599326 CrossrefGoogle Scholar[5] Zhang J., Liu W. and Wu Y., "Novel Technique for Vision-Based UAV Navigation," IEEE Transactions on Aerospace and Electronic Systems, Vol. 47, No. 4, 2011, pp. 2731–2741. https://doi.org/10.1109/TAES.2011.6034661 CrossrefGoogle Scholar[6] Ha J., Alvino C., Pryor G., Niethammer M., Johnson E. and Tannenbaum A., "Active Contours and Optical Flow for Automatic Tracking of Flying Vehicles," Proceedings 2004 American Control Conference, Vol. 4, June 2004, pp. 3441–3446. https://doi.org/10.23919/ACC.2004.1384442 Google Scholar[7] Weinstein A., Cho A., Loianno G. and Kumar V., "Visual Inertial Odometry Swarm: An Autonomous Swarm of Vision-Based Quadrotors," IEEE Robotics and Automation Letters, Vol. 3, No. 3, 2018, pp. 1801–1807. https://doi.org/10.1109/LRA.2018.2800119 CrossrefGoogle Scholar[8] Oh S. and Johnson E., "Relative Motion Estimation for Vision-Based Formation Flight Using Unscented Kalman Filter," Proceedings of AIAA Guidance, Navigation and Control Conference and Exhibit, AIAA Paper 2007-6866, 2007. https://doi.org/10.2514/6.2007-6866 LinkGoogle Scholar[9] Johnson E. N., Calise A. J., Sattigeri R., Watanabe Y. and Madyastha V., "Approaches to Vision-Based Formation Control," Proceedings of 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601), Vol. 2, IEEE, New York, 2004, pp. 1643–1648. https://doi.org/10.1109/CDC.2004.1430280 Google Scholar[10] Johnson E., Calise A., Watanabe Y., Ha J. and Neidhoefer J., "Real-Time Vision-Based Relative Aircraft Navigation," Journal of Aerospace Computing, Information, and Communication, Vol. 4, No. 4, 2007, pp. 707–738. https://doi.org/10.2514/1.23410 LinkGoogle Scholar[11] Pollini L., Mati R. and Innocenty M., "Experimental Evaluation of Vision Algorithms for Formation Flight and Aerial Refueling," Proceedings of AIAA Modeling and Simulation Technologies Conference and Exhibit, AIAA Paper 2004-4918, 2004. https://doi.org/10.2514/6.2004-4918 LinkGoogle Scholar[12] Martínez C., Richardson T. and Campoy P., "Towards Autonomous Air-to-Air Refuelling for UAVs Using Visual Information," Proceedings of 2013 IEEE International Conference on Robotics and Automation, IEEE, New York, 2013, pp. 5756–5762. https://doi.org/10.1109/ICRA.2013.6631404 Google Scholar[13] Campa G., Napolitano M. R. and Fravolini M. L., "Simulation Environment for Machine Vision Based Aerial Refueling for UAVs," IEEE Transactions on Aerospace and Electronic Systems, Vol. 45, No. 1, 2009, pp. 138–151. https://doi.org/10.1109/TAES.2009.4805269 CrossrefGoogle Scholar[14] Mondragón I. F., Campoy P., Martínez C. and Olivares-Méndez M. A., "3D Pose Estimation Based on Planar Object Tracking for UAVs Control," Proceedings of 2010 IEEE International Conference on Robotics and Automation, IEEE, New York, 2010, pp. 35–41. https://doi.org/10.1109/ROBOT.2010.5509287 Google Scholar[15] Wilson D. B., Göktoğan A. H. and Sukkarieh S., "A Vision Based Relative Navigation Framework for Formation Flight," Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), IEEE, New York, 2014, pp. 4988–4995. https://doi.org/10.1109/ICRA.2014.6907590 Google Scholar[16] Kelsey J. M., Byrne J., Cosgrove M., Seereeram S. and Mehra R. K., "Vision-Based Relative Pose Estimation for Autonomous Rendezvous and Docking," 2006 IEEE Aerospace Conference, IEEE, New York, 2006, p. 20. https://doi.org/10.1109/AERO.2006.1655916 Google Scholar[17] Redmon J., Divvala S., Girshick R. and Farhadi A., "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, New York, 2016, pp. 779–788. https://doi.org/10.1109/CVPR.2016.91 Google Scholar[18] Redmon J. and Farhadi A., "YOLOv3: An Incremental Improvement," arXiv preprint arXiv:1804.02767(2018), https://arxiv.org/abs/1804.02767. Google Scholar[19] Gonzalez R. C. and Woods R. E., Digital Image Processing, 2nd ed., Prentice-Hall, Hoboken, NJ, 2002, pp. 91–94. Google Scholar[20] Sonak M., Hlavac V. and Boyle R., Image Processing, Analysis, and Machine Vision, 3rd ed., Thomson, Toronto, 2008, pp. 176–180. Google Scholar[21] Otsu N., "A Threshold Selection Method from Gray-Level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, 1979, pp. 62–66. https://doi.org/10.1109/TSMC.1979.4310076 CrossrefGoogle Scholar[22] Harris C. and Stephens M., "A Combined Corner and Edge Detector," Proceedings of Alvey Vision Conference, Vol. 15, No. 50, 1988, pp. 147–151. https://doi.org/10.5244/c.2.23 Google Scholar[23] Ma Y., Soatto S., Kosecka J. and Sastry S. S., An Invitation to 3-D Vision: From Images to Geometric Models, Springer, New York, 2004, pp. 44–57. CrossrefGoogle Scholar[24] Groves P. D., Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, 1st ed., Artech House, London, 2008, pp. 38–50. Google Scholar[25] Cook M. V., Flight Dynamics Principles, 3rd ed., Butterworth-Heinemann, Oxford, 2013, pp. 19–23. Google Scholar[26] Deliyannis T., Sun Y. and Fidler J. K., Continuous-Time Active Filter Design, CRC Press, Boca Raton, FL, 1998, pp. 38–52. Google Scholar Previous article FiguresReferencesRelatedDetailsCited byHeuristic EPnP-Based Pose Estimation for Underground Machine Tracking15 February 2022 | Symmetry, Vol. 14, No. 2 What's Popular Volume 18, Number 9September 2021 Metrics CrossmarkInformationCopyright © 2021 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. All requests for copying and permission to reprint should be submitted to CCC at www.copyright.com; employ the eISSN 2327-3097 to initiate your request. See also AIAA Rights and Permissions www.aiaa.org/randp. TopicsAircraft Components and StructureAircraft DesignAircraft Operations and TechnologyAircraft Stability and ControlAircraft SystemsAircraftsFixed-Wing AircraftFlight Control SurfacesQuadcopterRotorcraftsUnmanned Aerial Vehicle KeywordsFixed Wing AircraftFiducial MarkerGNSSHarris Corner DetectorHistogramsEarth Centered Earth FixedConvolutional Neural NetworkLight Sport AircraftAttitude and Heading Reference SystemYawAcknowledgmentThis work was supported by the Technology Innovation Program (20002712, Advanced Pilot Assistant System Development based on Multiple Surveillance Sensors and Deep Learning for Manned and Unmanned Aircraft Systems) funded by the Ministry of Trade, Industry & Energy (Republic of Korea).PDF Received10 February 2021Accepted15 April 2021Published online21 May 2021

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: aucune
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,046

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0140,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,241
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations5
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Aerospace Information SystemsMême sujetRobotics and Sensor-Based LocalizationTravaux en français237 207