{"paper_id":"4067e18c-41be-42ce-b935-6173b3a09045","body_text":"Experimental Verification of a Monocular CNN-Based Pose Estimation Algorithm for the SROC Mission | 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 Experimental Verification of a Monocular CNN-Based Pose Estimation Algorithm for the SROC Mission Antonio D'Ortona, Lucrezia Lovaglio, Marco Visca, Fabrizio Stesina, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8545599/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract This work presents a Convolutional Neural Network (CNN)-based framework for monocular pose estimation of spacecraft in the context of the ESA Space Rider Observer CubeSat (SROC) mission. The mission, led by a consortium of Italian institutions, involves autonomous proximity operations including inspection and docking maneuvers with the Space Rider vehicle. Given the constraints of CubeSat-class hardware, we explore an efficient and adaptable CNN-based solution for relative navigation using only optical input. In order to address the challenges of domain gap between synthetic and real data, a high-fidelity synthetic dataset was generated using Blender with realistic lighting conditions and applying dataset augmentation techniques. Real image datasets were acquired at ESA’s GRALS facility with a COTS camera to test the trained models with a 1:20 mockup of Space Rider. A hyperparameter optimization (HPO) was conducted using the Optuna framework to enhance training performance. Multiple training strategies were evaluated, including fine-tuning techniques and dataset mixing approaches, to enhance performances on a specific maneuver that requires greater accuracy. The trained networks were deployed on the Payload Processing Unit using the ONNX Runtime framework for inference. Tests demonstrated consistent and reliable performance across multiple scenarios. These results confirm that, when supported by domain adaptation and hyperparameter optimization, the proposed CNN architecture can achieve accurate pose estimation under limited computational resources, making it a strong candidate for future autonomous CubeSat applications. On-Orbit Servicing (OOS) rendezvous and docking pose estimation visual navigation Convolutional Neural Network (CNN) hyperparameter optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviews received at journal 27 Apr, 2026 Reviewers agreed at journal 03 Apr, 2026 Reviewers invited by journal 19 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 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-8545599\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":576833722,\"identity\":\"d0231e78-f011-4503-bdc7-1e925f4a7647\",\"order_by\":0,\"name\":\"Antonio 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The mission, led by a consortium of Italian institutions, involves autonomous proximity operations including inspection and docking maneuvers with the Space Rider vehicle. Given the constraints of CubeSat-class hardware, we explore an efficient and adaptable CNN-based solution for relative navigation using only optical input.\\u003c/p\\u003e\\n\\u003cp\\u003eIn order to address the challenges of domain gap between synthetic and real data, a high-fidelity synthetic dataset was generated using Blender with realistic lighting conditions and applying dataset augmentation techniques. Real image datasets were acquired at ESA’s GRALS facility with a COTS camera to test the trained models with a 1:20 mockup of Space Rider. A hyperparameter optimization (HPO) was conducted using the Optuna framework to enhance training performance. 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