LiDAR-Camera Fusion Methods for Long-Distance Rail Transit Perception

LiDAR-Camera Fusion Methods for Long-Distance Rail Transit Perception | 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 Article LiDAR-Camera Fusion Methods for Long-Distance Rail Transit Perception Xin Liu, HongPing Wang, Shouxin Ruan, Linsen Song, Yiwen Zhang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7144022/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract In autonomous driving systems, sensor-based environmental perception is paramount. However, in long-distance perception for rail transit, the extrinsic calibration of LiDAR and telephoto cameras is hindered by sparse point clouds and intrinsic parameter inaccuracies. To address these challenges, we propose a novel calibration board design and a corresponding joint extrinsic calibration method. Inspired by engineering positioning principles, this calibration board builds upon the traditional checkerboard by integrating circular positioning holes. By coupling spatial re-projection constraints with geometric feature alignment, the proposed approach markedly improves feature point extraction and 2D–3D correspondences. Experimental results demonstrate that the method substantially enhances both calibration accuracy and efficiency, offering solid technical support for environmental perception in rail transit. Physical sciences/Engineering Physical sciences/Mathematics and computing Train safety LiDAR–camera calibration Nonlinear optimization 3D chessboard corners Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Feb, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 04 Sep, 2025 Reviews received at journal 27 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 11 Aug, 2025 Reviewers agreed at journal 10 Aug, 2025 Reviewers invited by journal 29 Jul, 2025 Editor invited by journal 23 Jul, 2025 Editor assigned by journal 21 Jul, 2025 Submission checks completed at journal 18 Jul, 2025 First submitted to journal 16 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-7144022","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":492863575,"identity":"171b6fef-8cc9-478d-ae7e-4033aaf491c2","order_by":0,"name":"Xin Liu","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":492863576,"identity":"352eeaf4-99cf-48e4-aee1-a7205d5846f8","order_by":1,"name":"HongPing 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