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Record 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 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Aerospace Information Systems · 2021
Typearticle
Languageen
FieldEngineering
TopicRobotics and Sensor-Based Localization
Canadian institutionsnot available
FundersKorea Evaluation Institute of Industrial Technology
KeywordsFixed wingPerspective (graphical)PoseComputer visionComputer scienceEstimationArtificial intelligenceWingEngineeringAerospace engineering

Abstract

fetched live from 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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.594
Threshold uncertainty score0.487

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.241
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it