A Precise Urban Vehicle Navigation Method Based on 3D Velocity Mamba Constraint

A Precise Urban Vehicle Navigation Method Based on 3D Velocity Mamba Constraint | 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 A Precise Urban Vehicle Navigation Method Based on 3D Velocity Mamba Constraint Feifan Lin, Qingzhong Cai, Huizheng Yuan, Yue Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7170081/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract In complex urban environments, vehicle-integrated navigation systems based on Global Navigation Satellite Systems (GNSS) aided Inertial Navigation Systems (INS) typically rely on nonholonomic constraints (NHC) and odometer velocity to improve navigation accuracy. However, the performance of such systems is often degraded due to inaccurate estimation of the installation angles between the Inertial Measurement Unit (IMU) and the vehicle frame. To address this challenge, this paper proposes a precise urban vehicle navigation method based on a 3D velocity constraint using the Mamba model. The proposed GNSS/INS/Mamba-ODO framework learns a direct and efficient mapping from raw IMU outputs to 3D velocity through asymmetric sequence segmentation, multi-scale temporal modeling, and a state-space sequence learning backbone. By replacing traditional velocity constraints, the method significantly enhances positioning robustness in GNSS-denied urban areas. Ground vehicle experiments demonstrate that the proposed approach achieves a mean Root Mean Square Error (RMSE) of 0.22 m/s in 3D velocity estimation. Furthermore, during a 120 sGNSS outage, the 3D positioning RMSE remains within 0.20 m, confirming the accuracy and robustness of the proposed method in real-world urban scenarios. Urban Vehicle Navigation GNSS/INS 3D Velocity Mamba Deep learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 02 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 01 Sep, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 21 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers agreed at journal 18 Aug, 2025 Reviewers invited by journal 18 Aug, 2025 Editor assigned by journal 12 Aug, 2025 Submission checks completed at journal 21 Jul, 2025 First submitted to journal 20 Jul, 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-7170081","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504691485,"identity":"59c5f8ba-7469-4232-8b3b-4cf8d3622ec5","order_by":0,"name":"Feifan Lin","email":"","orcid":"","institution":"Beihang University","correspondingAuthor":false,"prefix":"","firstName":"Feifan","middleName":"","lastName":"Lin","suffix":""},{"id":504691486,"identity":"c26c6db5-fd53-45e3-b9b2-611ac406687e","order_by":1,"name":"Qingzhong Cai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEUlEQVRIiWNgGAWjYDACZiBmbIByEioOQBg8xGs5Q4wWBmQtjG1EaOE7znv45c8dNnnyDswPPzycdyexv/0A44O3bQzy5ji0SB7mS7OQPJNWbHiAzVgicduzxBlnEpgN57YxGO5swK7F4DCPmYFh2+HEjQ08DEAthxMbDiSwSfO2MSQYHMCjJRGihflH4pzDifPPP2D/TUCL8YODQC3zGXjYJBIbDiduuJHAxoxPiyTQFsbGtrTEDQxsZhYJx54Zb7zxsFlyzjkJww04tPCdP2P88WebTeL8BubHN3/U3JGddz754Ic3ZTbyuGxhOMDAJgF24f0HMCFwNEngUA/WwvwBRMs34FYzCkbBKBgFIxwAAK2JZV5DQ8fxAAAAAElFTkSuQmCC","orcid":"","institution":"Beihang University","correspondingAuthor":true,"prefix":"","firstName":"Qingzhong","middleName":"","lastName":"Cai","suffix":""},{"id":504691487,"identity":"3b0d81a3-26fa-4281-bee6-b878d2b5ab63","order_by":2,"name":"Huizheng Yuan","email":"","orcid":"","institution":"Hubei University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Huizheng","middleName":"","lastName":"Yuan","suffix":""},{"id":504691489,"identity":"e892601e-af95-4a21-820f-f0eedbc3f5cb","order_by":3,"name":"Yue Yu","email":"","orcid":"","institution":"Hong Kong Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-07-20 13:53:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7170081/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7170081/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90075234,"identity":"213a9e10-e179-48af-9a3b-55e9b6157fcc","added_by":"auto","created_at":"2025-08-28 07:53:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4239819,"visible":true,"origin":"","legend":"","description":"","filename":"20250720.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7170081/v1_covered_c2fb212e-6014-4dec-a82b-1968ed0d9628.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Precise Urban Vehicle Navigation Method Based on 3D Velocity Mamba Constraint","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":"urban-informatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Urban Informatics](https://link.springer.com/journal/44212)","snPcode":"4212","submissionUrl":"https://submission.springernature.com/new-submission/44212/3","title":"Urban Informatics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Urban Vehicle Navigation, GNSS/INS, 3D Velocity Mamba, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-7170081/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7170081/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn complex urban environments, vehicle-integrated navigation systems based on Global Navigation Satellite Systems (GNSS) aided Inertial Navigation Systems (INS) typically rely on nonholonomic constraints (NHC) and odometer velocity to improve navigation accuracy. 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