Enhanced Real-Time 6D Pose Estimation for Automatic Recovery of In-Flight UAVs Using Distance-Aware Keypoint Heatmaps

Enhanced Real-Time 6D Pose Estimation for Automatic Recovery of In-Flight UAVs Using Distance-Aware Keypoint Heatmaps | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced Real-Time 6D Pose Estimation for Automatic Recovery of In-Flight UAVs Using Distance-Aware Keypoint Heatmaps Mingyu Jeong, Andrew Jaeyong Choi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7356051/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Unmanned Aerial Vehicles (UAVs) play crucial roles across various fields but face significant challenges when control is lost or securing a safe runway is not feasible. Under these circumstances, reliable UAV recovery systems are essential for safe retrieval, requiring precise and real-time localization. This paper proposes an automatic in-fight UAV recovery system using an unmanned ground vehicle when securing safe runway is challenging such as unstructured road, and presents a practical approach for real-time six-degree-of-freedom (6-DOF) pose estimation of an in-flight UAV using monocular RGB images and deep learning-based heatmap keypoint detection. To enhance the accuracy of the pose estimation, we propose an Adaptive Sigma technique for keypoint detection, which adjusts the sigma values of the keypoint heatmap based on the distance from the camera to UAV. Thus, the proposed method can robustly adapt the changes in the distance of UAV to improve the keypoint localization performance. By utilizing the predicted keypoints in a Perspective-n-Point (PnP) algorithm, the 6-DOF pose information of the UAV can be obtained in real time. The proposed Adaptive Sigma to heatmap-based keypoint detection improves the Percentage of Correct Keypoints (PCK) by up to 2.5%, with consistently novel performance across various state-of-the-art backbone architectures. The proposed method qualitatively evaluated in challenging scenarios such as various altitude, significant tilt, and motion blur. Physical sciences/Engineering Physical sciences/Mathematics and computing Physical sciences/Optics and photonics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Dec, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 21 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviews received at journal 05 Sep, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 22 Aug, 2025 Editor invited by journal 18 Aug, 2025 Editor assigned by journal 14 Aug, 2025 Submission checks completed at journal 13 Aug, 2025 First submitted to journal 12 Aug, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7356051","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":504020466,"identity":"bd815d1a-b009-42db-9b35-5608ab9775f8","order_by":0,"name":"Mingyu Jeong","email":"","orcid":"","institution":"Gachon University","correspondingAuthor":false,"prefix":"","firstName":"Mingyu","middleName":"","lastName":"Jeong","suffix":""},{"id":504020467,"identity":"5af20fb2-0bef-45d7-ad9a-edcf0dd65d9c","order_by":1,"name":"Andrew Jaeyong Choi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAElEQVRIiWNgGAWjYHACAyBmA5KMjQ+ArASoaAIu5Shamg1I0QIm2SSI0sI/u3nbh49tfPLm7M1tlV9q7PL4pRsYP/xgSMvHpUXizrHimTPb2Ax39hxsuy1zLLlYcs4BZskehhzLBlx6buQYM/NuY0swuJHYdluC7UDihhsJDNIMDBUGuHTIg7T8hWoplvgH1sL8G58WA5AWRqgWxo9tYC1sQFtycGoxvJFWzNj7j81ww5mDzdKMfcmJM2cktln2GKTh1CJ3I3kzw48zx+QNjrc//Pjjm11iv0Ty4Rs/KpJxaoGCY2CSmQdMMTbAIgsfqIGo/UFQ4SgYBaNgFIxEAAA2NVqowVBrLwAAAABJRU5ErkJggg==","orcid":"","institution":"Gachon University","correspondingAuthor":true,"prefix":"","firstName":"Andrew","middleName":"Jaeyong","lastName":"Choi","suffix":""}],"badges":[],"createdAt":"2025-08-12 13:08:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7356051/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7356051/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-31572-3","type":"published","date":"2025-12-10T15:57:08+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":98243472,"identity":"e31db971-3798-4b5b-9107-52a719aa9944","added_by":"auto","created_at":"2025-12-15 16:04:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3561722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7356051/v1_covered_cf06b31e-364c-4926-9748-51c8f61461da.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Real-Time 6D Pose Estimation for Automatic Recovery of In-Flight UAVs Using Distance-Aware Keypoint Heatmaps","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7356051/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7356051/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Unmanned Aerial Vehicles (UAVs) play crucial roles across various fields but face significant challenges when control is lost or securing a safe runway is not feasible. 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