A High-precision and High-robust Lidar-inertial SLAM Method Suitable for Robot Operation Scenarios

A High-precision and High-robust Lidar-inertial SLAM Method Suitable for Robot Operation 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 A High-precision and High-robust Lidar-inertial SLAM Method Suitable for Robot Operation Scenarios Baoliang Wang, Quanyu Wu, Yu Liu, Huayong Zhang, Xiaojie Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6565085/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This paper proposes a tightly-coupled lidar-inertial odometry simultaneous localization and mapping (SLAM) framework, aiming to achieve high-precision and high-robustness position estimation and high-quality map construction in robot operation scenarios. In the feature extraction process, we innovatively adopt a face-based sampling strategy combined with uniform point cloud filtering to effectively optimize the spatial distribution characteristics of the point cloud data. When using the lidar odometry to correct the inertial measurement unit (IMU) data, we designed a lidar odometry confidence model based on the degree of degradation assessment, realized the optimization of the sensor fusion parameters through the dynamic noise covariance adjustment mechanism, and corrected the system state in real time by re-propagating the inertial data. In the optimization stage, we modify the residual computation model to appropriately increase the corner point matching weight and suppress the far-point noise. The proposed method is extensively evaluated and tested on the M2DGR dataset covering indoor and outdoor multi-scenes. Our scheme demonstrates advantages in terms of robustness and long-term error accumulation. lidar-inertial odometry tightly-coupled robot operation simultaneous localization and mapping Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Jul, 2025 Reviews received at journal 27 Jul, 2025 Reviewers agreed at journal 23 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers agreed at journal 21 Jul, 2025 Reviewers invited by journal 21 Jul, 2025 Editor assigned by journal 12 May, 2025 Submission checks completed at journal 05 May, 2025 First submitted to journal 30 Apr, 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. 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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-6565085","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":488752701,"identity":"1f44bf47-2df1-4928-ae88-3b6e3be0c1ea","order_by":0,"name":"Baoliang Wang","email":"","orcid":"","institution":"Jiangsu University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Baoliang","middleName":"","lastName":"Wang","suffix":""},{"id":488752702,"identity":"90b3b8b2-a89f-4cc8-b759-9f5973b68f9c","order_by":1,"name":"Quanyu 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