{"paper_id":"29d06dcf-8705-4e3e-b42f-d3d3d7eb0f1d","body_text":"FedS-SLAM: A Federated Semantic Collaborative SLAM System for UAV Swarms in Dynamic | 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 FedS-SLAM: A Federated Semantic Collaborative SLAM System for UAV Swarms in Dynamic Zhi-jian Yan, Xin-e Li, Chun-sheng Cui, Zhi-wei Zeng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7409201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Existing SLAM technologies struggle to meet the demands of drone swarms in dynamic, GPS-denied environments. To address this, we propose FedS-SLAM, a federated semantic cooperative SLAM system designed specifically for drone swarms. It innovatively employs a federated learning architecture to drastically reduce communication overhead by exchanging only lightweight \"federated features.\" The system's core is a deeply coupled optimization loop where the SLAM back-end provides high-quality data to train a shared model via federated learning, which in turn enhances the SLAM front-end. This synergy enables the precise filtering of dynamic objects, significantly improving localization robustness. Experiments on multiple public benchmark datasets prove that FedS-SLAM outperforms state-of-the-art systems in both trajectory accuracy and map quality. Robotics Collaborative SLAM Federated Learning Dynamic Environments UAV Swarm Semantic SLAM Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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. 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