RoboNavGuard: Lightweight Deformable Obstacle Segmentation and 3D Visual Grounding for Indoor Robot Navigation

RoboNavGuard: Lightweight Deformable Obstacle Segmentation and 3D Visual Grounding for Indoor Robot Navigation | 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 RoboNavGuard: Lightweight Deformable Obstacle Segmentation and 3D Visual Grounding for Indoor Robot Navigation Zehan Tan, Yaoyang Wu, Henghua Shen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7809650/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Navigating obstacles and understanding complex environments remain critical challenges in the deployment of automated guided vehicles (AGVs), a key area in mobile robotics. Existing AGVs often struggle with inadequate recognition of specific objects, such as plastic bags and feces, which may cause issues such as roller entanglements or contamination. Furthermore, vision-language navigation discrepancies can lead to positioning errors. In this paper, we propose a deep learning-based solution (called `` RoboNavGuard '') specifically designed to address these issues and improve the practical deployment of AGVs. The proposed RoboNavGuard method builds on the PyraBiNet++ architecture for precise obstacle segmentation. The RoboNavGuard is composed of the following three key innovations: (a) a novel architecture that fuses local and global features for obstacle segmentation, (b) a practical and comprehensive dataset that establishes a new benchmark for 3D visual grounding, and (c) a method that tightly integrates textual commands into AGVs’ visual perception, thereby enabling more natural and efficient human-robot interaction.We also introduce a new dataset, called `` FragCloud3DRef++ '', for training our `` Re_3DVG-Small '' model, a lightweight 3D visual grounding model designed to enhance fragmented point cloud understanding and improve language–vision navigation.Our system is being deployed in live AGV operations to validate its real-world applicability. The code source and dataset are open at {\bf https://github.com/zehantan6970/RoboNavGuard }. Automated Guided Vehicles (AGV) Deep Learning Mobile Robot Obstacle Navigation Obstacle Segmentation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Feb, 2026 Reviews received at journal 17 Jan, 2026 Reviews received at journal 09 Jan, 2026 Reviews received at journal 26 Dec, 2025 Reviewers agreed at journal 18 Dec, 2025 Reviewers agreed at journal 17 Dec, 2025 Reviewers invited by journal 17 Dec, 2025 Editor assigned by journal 09 Oct, 2025 Submission checks completed at journal 09 Oct, 2025 First submitted to journal 08 Oct, 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-7809650","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562411236,"identity":"64df3a48-abc4-4959-a370-ec5a9ba5839f","order_by":0,"name":"Zehan Tan","email":"","orcid":"","institution":"Macau Millennium College","correspondingAuthor":false,"prefix":"","firstName":"Zehan","middleName":"","lastName":"Tan","suffix":""},{"id":562411237,"identity":"bd26516d-0445-4dbf-bf93-be708c7c1446","order_by":1,"name":"Yaoyang Wu","email":"","orcid":"","institution":"Macau Millennium 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Navigation","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":"machine-vision-and-applications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mvap","sideBox":"Learn more about [Machine Vision and Applications](https://www.springer.com/journal/138)","snPcode":"138","submissionUrl":"https://submission.springernature.com/new-submission/138/3","title":"Machine Vision and Applications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer 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Existing AGVs often struggle with inadequate recognition of specific objects, such as plastic bags and feces, which may cause issues such as roller entanglements or contamination. Furthermore, vision-language navigation discrepancies can lead to positioning errors. In this paper, we propose a deep learning-based solution (called ``\u003cem\u003eRoboNavGuard\u003c/em\u003e'') specifically designed to address these issues and improve the practical deployment of AGVs. The proposed \u003cem\u003eRoboNavGuard\u003c/em\u003e method builds on the PyraBiNet++ architecture for precise obstacle segmentation. The \u003cem\u003eRoboNavGuard\u003c/em\u003e is composed of the following three key innovations: (a) a novel architecture that fuses local and global features for obstacle segmentation, (b) a practical and comprehensive dataset that establishes a new benchmark for 3D visual grounding, and (c) a method that tightly integrates textual commands into AGVs\u0026rsquo; visual perception, thereby enabling more natural and efficient human-robot interaction.We also introduce a new dataset, called ``\u003cem\u003eFragCloud3DRef++\u003c/em\u003e'', for training our ``\u003cem\u003eRe_3DVG-Small\u003c/em\u003e'' model, a lightweight 3D visual grounding model designed to enhance fragmented point cloud understanding and improve language\u0026ndash;vision navigation.Our system is being deployed in live AGV operations to validate its real-world applicability. 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