Lidar-Inertial SLAM Method Integrated with Visual QR Codes for Indoor Mobile Robots

Lidar-Inertial SLAM Method Integrated with Visual QR Codes for Indoor Mobile Robots | 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-Inertial SLAM Method Integrated with Visual QR Codes for Indoor Mobile Robots Lin Yang, Yi Tao, Mohan Li, Juncheng Zhou, Kan Jiao, Zhiwei Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259914/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Multi-modal sensor fusion-based LiDAR SLAM is a critical capability for mobile robots to achieve autonomous navigation in complex environments. However, challenges persist when operating in indoor settings with features such as long corridors, dynamic objects, repetitive or symmetric structures, highly similar scenarios, or sparse features, which often result in decreased localization accuracy and cumulative errors. To address these issues, we propose a LiDAR-inertial SLAM method enhanced with visual QR codes. Specifically, our approach constructs a SLAM framework comprising a front-end LiDAR-inertial odometry module based on an Extended Kalman Filter (EKF) and a back-end global factor graph optimization. By incorporating visual QR codes as additional landmarks in the back-end, this framework not only supplies extra localization references for LiDAR SLAM, but also improves system stability in feature-sparse environments. In this study, we integrate environmental features derived from multiple sensors, effectively boosting the accuracy and robustness of both robot localization and mapping while mitigating localization errors caused by insufficient LiDAR features. The proposed system has been extensively tested in various indoor scenarios. Experimental results demonstrate that our LiDAR-inertial SLAM method, which incorporates visual QR codes, significantly enhances localization accuracy and bolsters the stability and robustness of map construction under feature-sparse and dynamic indoor conditions. Physical sciences/Engineering Physical sciences/Mathematics and computing Lidar-Inertial SLAM Multi-modal sensor fusion Visual QR code Factor graph optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 06 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 12 Nov, 2025 Reviews received at journal 08 Nov, 2025 Reviewers agreed at journal 02 Nov, 2025 Reviewers agreed at journal 29 Oct, 2025 Editor invited by journal 27 Oct, 2025 Reviewers invited by journal 28 Apr, 2025 Editor assigned by journal 10 Apr, 2025 Submission checks completed at journal 26 Mar, 2025 First submitted to journal 26 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-6259914","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":449004994,"identity":"a21d8b7f-95ce-479b-8187-a17df641ad4c","order_by":0,"name":"Lin 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