Multi-Sensor Information Fusion Methods for Coal Mine Exploration in GNSS-Denied Scenarios

Multi-Sensor Information Fusion Methods for Coal Mine Exploration in GNSS-Denied Scenarios | 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 Multi-Sensor Information Fusion Methods for Coal Mine Exploration in GNSS-Denied Scenarios Shuai Wang, Shengtao Liao, Yuntao Liang, Shuai Xiao, Liao Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6217705/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Dec, 2025 Read the published version in EURASIP Journal on Wireless Communications and Networking → Version 1 posted 5 You are reading this latest preprint version Abstract In coal exploration scenarios such as underground mines and enclosed tunnels, Global Navigation Satellite System (GNSS) signals are often unavailable due to geological obstacles, while challenges like dust interference, dynamic obstacles, and extreme lighting variations further degrade localization accuracy. To address these issues, this paper proposes a novel multi-sensor fusion framework named LiVIC-EKF (LiDAR-Visual-Inertial Covariance-adaptive Extended Kalman Filter). The algorithm integrates LiDAR, binocular cameras, and an Inertial Measurement Unit (IMU) to achieve robust six-degree-of-freedom pose estimation. Key steps include offline calibration of sensor parameters, extraction of LiDAR geometric features using an improved LOAM algorithm, and tight-coupling optimization of visual-inertial odometry via VINS-Fusion. The LiVIC-EKF framework adaptively weights the contributions of LiDAR and visual-inertial odometry through a dynamic covariance mechanism, ensuring stable localization in complex environments. Experimental results on the KITTI dataset demonstrate that LiVIC-EKF reduces the mean Absolute Pose Error (APE) to 2.60 meters and standard deviation to 1.66 meters, outperforming state-of-the-art methods such as A-LOAM, FAST-LIO2, and VINS-Fusion. This approach provides a reliable solution for coal exploration applications where GNSS is unavailable. Multi-sensor Fusion Extended Kalman Filter (EKF) Visual-Inertial Odometry LiDAR Odometry Coal Mine Exploration GNSS-Denied Scenarios Full Text Cite Share Download PDF Status: Published Journal Publication published 10 Dec, 2025 Read the published version in EURASIP Journal on Wireless Communications and Networking → Version 1 posted Editorial decision: Major revision 23 Apr, 2025 Reviewers agreed at journal 31 Mar, 2025 Reviewers invited by journal 31 Mar, 2025 Editor assigned by journal 24 Mar, 2025 First submitted to journal 24 Mar, 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-6217705","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436586547,"identity":"35a845f4-240d-4236-8caa-474b5ae4d679","order_by":0,"name":"Shuai Wang","email":"","orcid":"","institution":"Fushun China Coal Technology Engineering Testing Center Co Ltd","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Wang","suffix":""},{"id":436586548,"identity":"bb0e51f4-a79e-42b9-8f80-23a23915606c","order_by":1,"name":"Shengtao Liao","email":"","orcid":"","institution":"South China Normal 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