{"paper_id":"411a92d3-9e08-45f1-8306-78bc98510fd0","body_text":"Viewpoint-Aware Pose Estimation Framework for Cooperative UAVs | 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 Research Article Viewpoint-Aware Pose Estimation Framework for Cooperative UAVs Youngrun Kim, Heokjune You, Seunghyun Choi, Dongwon Jung This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9075640/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Pose estimation from monocular vision is essential for UAV applications, yetexisting methods often struggle in real-world settings. Traditional techniquesbased on markers or hand-crafted features are computationally efficient but unre-liable in cluttered or unstructured environments. Learning-based approaches,while powerful, typically demand extensive target-specific annotation andretraining, limiting their generalizability. This paper proposes a robust, key-point training-free pose estimation framework that leverages pre-trained visualcorrespondence models—SuperPoint and LightGlue—to eliminate the need fortask-specific keypoint detection or pose regression. Target localization is per-formed using an off-the-shelf YOLO detector, followed by viewpoint-awaretemplate matching to discretize target appearance under varying views. Withinthe detected region, SuperPoint features are matched via LightGlue, and anovel Coverage Score evaluates the spatial distribution of correspondences toreject degenerate configurations prior to PnP–RANSAC pose recovery. AnUnscented Kalman Filter integrates asynchronous measurements for tempo-rally stable yet responsive estimates. The framework requires no target-specificretraining, enabling seamless deployment across diverse targets and environ-ments. Extensive evaluations—including indoor, outdoor, and visually degradedscenarios—demonstrate robust, consistent performance and significant reduc-tions in annotation effort and system integration complexity, outperformingstate-of-the-art learning-based methods in various conditions. 6-DoF pose estimation Template based Monocular vision Feature correspondence Kalman filter Unmanned aerial vehicle(UAV) applications Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 30 Mar, 2026 Editor assigned by journal 22 Mar, 2026 Submission checks completed at journal 22 Mar, 2026 First submitted to journal 09 Mar, 2026 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9075640\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":614837079,\"identity\":\"4e0e3713-b353-49e2-83cc-f606cb0510c0\",\"order_by\":0,\"name\":\"Youngrun Kim\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Korea Aerospace University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Youngrun\",\"middleName\":\"\",\"lastName\":\"Kim\",\"suffix\":\"\"},{\"id\":614837080,\"identity\":\"9859da70-5e49-4c51-84ee-1aba51d3f356\",\"order_by\":1,\"name\":\"Heokjune You\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Korea Aerospace University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Heokjune\",\"middleName\":\"\",\"lastName\":\"You\",\"suffix\":\"\"},{\"id\":614837081,\"identity\":\"2179293b-5d60-4315-a75d-ed8173908554\",\"order_by\":2,\"name\":\"Seunghyun Choi\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Korea Aerospace University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Seunghyun\",\"middleName\":\"\",\"lastName\":\"Choi\",\"suffix\":\"\"},{\"id\":614837082,\"identity\":\"e24d2230-a563-4082-932a-87de760e48c1\",\"order_by\":3,\"name\":\"Dongwon Jung\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDCCAweAREUCjEe0ljOkaQFixjZStPAdPGP28Ou8NHmDA8wPPzCcuUdYi+SBM+bGsttyDDccYDOWYLhRTFiLwYEzZtKS2yoYNxxgMGNg+JBAUAdUy5wK+w0H2L8Rr0XyY0NO4oYDPEBbbhChRfLAsTJphmNpyTMP8xRLJMBDGw/gu3F4m+SPmmTbvuPtGz98OEaEFgaJAwzMPCAGMxATo4GBgb+BgfEHUSpHwSgYBaNgxAIADhJBkkBP184AAAAASUVORK5CYII=\",\"orcid\":\"\",\"institution\":\"Korea Aerospace University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Dongwon\",\"middleName\":\"\",\"lastName\":\"Jung\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-09 17:08:18\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9075640/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9075640/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106093087,\"identity\":\"52469a87-e4d9-408f-be69-40e8efca67a9\",\"added_by\":\"auto\",\"created_at\":\"2026-04-03 11:33:56\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2466052,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"JINT20260316.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9075640/v1_covered_7c8aa47f-a088-4c0f-9219-17819d328389.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Viewpoint-Aware Pose Estimation Framework for Cooperative UAVs\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-intelligent-and-robotic-systems\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Journal of Intelligent \\u0026 Robotic Systems](https://link.springer.com/journal/10846)\",\"snPcode\":\"10846\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10846/3\",\"title\":\"Journal of Intelligent \\u0026 Robotic Systems\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"6-DoF pose estimation, Template based, Monocular vision, Feature correspondence, Kalman filter, Unmanned aerial vehicle(UAV) applications\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9075640/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9075640/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Pose estimation from monocular vision is essential for UAV applications, yetexisting methods often struggle in real-world settings. Traditional techniquesbased on markers or hand-crafted features are computationally efficient but unre-liable in cluttered or unstructured environments. Learning-based approaches,while powerful, typically demand extensive target-specific annotation andretraining, limiting their generalizability. This paper proposes a robust, key-point training-free pose estimation framework that leverages pre-trained visualcorrespondence models—SuperPoint and LightGlue—to eliminate the need fortask-specific keypoint detection or pose regression. Target localization is per-formed using an off-the-shelf YOLO detector, followed by viewpoint-awaretemplate matching to discretize target appearance under varying views. Withinthe detected region, SuperPoint features are matched via LightGlue, and anovel Coverage Score evaluates the spatial distribution of correspondences toreject degenerate configurations prior to PnP–RANSAC pose recovery. AnUnscented Kalman Filter integrates asynchronous measurements for tempo-rally stable yet responsive estimates. The framework requires no target-specificretraining, enabling seamless deployment across diverse targets and environ-ments. Extensive evaluations—including indoor, outdoor, and visually degradedscenarios—demonstrate robust, consistent performance and significant reduc-tions in annotation effort and system integration complexity, outperformingstate-of-the-art learning-based methods in various conditions.\",\"manuscriptTitle\":\"Viewpoint-Aware Pose Estimation Framework for Cooperative UAVs\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-04-01 02:56:38\",\"doi\":\"10.21203/rs.3.rs-9075640/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-30T20:00:19+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-22T23:25:27+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-22T23:25:02+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Journal of Intelligent \\u0026 Robotic Systems\",\"date\":\"2026-03-09T16:58:38+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"journal-of-intelligent-and-robotic-systems\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"Learn more about [Journal of Intelligent \\u0026 Robotic Systems](https://link.springer.com/journal/10846)\",\"snPcode\":\"10846\",\"submissionUrl\":\"https://submission.springernature.com/new-submission/10846/3\",\"title\":\"Journal of Intelligent \\u0026 Robotic Systems\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Springer Open\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"7b737d20-b639-45c4-a4be-1b889937fd61\",\"owner\":[],\"postedDate\":\"April 1st, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-04-01T02:56:38+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-04-01 02:56:38\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9075640\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9075640\",\"identity\":\"rs-9075640\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}