Adaptive Feature Regulation and Directional Consistency Optimization for Lightweight ORB-SLAM3 in Resource-Constrained Indoor Environments

Adaptive Feature Regulation and Directional Consistency Optimization for Lightweight ORB-SLAM3 in Resource-Constrained Indoor Environments | 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 Adaptive Feature Regulation and Directional Consistency Optimization for Lightweight ORB-SLAM3 in Resource-Constrained Indoor Environments Runping Zheng, Zhuhua Hu, Chunyi Yue, Yaochi Zhao, Zican Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7989901/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Visual Simultaneous Localization and Mapping (VSLAM) is a foundational technology widely applied in robotics, autonomous driving, and augmented reality, enabling devices to navigate and construct environmental models using visual sensors. However, on resource-constrained hardware platforms, the real-time performance and robustness of VSLAM systems are severely hindered by limited computational resources, especially in indoor environments where illumination changes and dynamic scenes cause significant fluctuations in feature point quantity, undermining tracking stability and increasing backend optimization overhead. To address these challenges, we propose FCD-VSLAM, a lightweight ORB-SLAM3 algorithm integrated with feature point convergence adaptation and pose-based directional consistency checks. Specifically, we design a Feature-Point Convergence Adaptive Module that combines image enhancement, non-maximum suppression, and multi-objective optimization to dynamically determine the optimal feature point range. A Directional Consistency Check Skip Mechanism based on a motion model is introduced to reduce redundant computations during stable pose changes, and a Descriptor Optimization Method fusing directional information is proposed to improve matching accuracy. Evaluations on the EuRoC public dataset and a private dataset show that FCD-VSLAM outperforms the original ORB-SLAM3 and other lightweight SLAM methods, achieving an average 20.18% improvement in localization accuracy (ATE RMSE) on EuRoC sequences and a 12.45% reduction in per-frame processing time. This work advances the practical application of VSLAM on resource-limited devices, providing a reliable solution for real-time environmental perception in scenarios such as indoor robotics and portable augmented reality systems.The code are available at https://github.com/zcrop/FCD-SLAM . ORB-SLAM3 Limited resources Directional consistency check Multi-objective optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7989901","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":550235209,"identity":"8863174d-68b7-46be-afe1-f45ac20a47d8","order_by":0,"name":"Runping Zheng","email":"","orcid":"","institution":"Hainan University","correspondingAuthor":false,"prefix":"","firstName":"Runping","middleName":"","lastName":"Zheng","suffix":""},{"id":550235210,"identity":"0c9e7312-02a6-447c-9e15-7dde13b4693b","order_by":1,"name":"Zhuhua 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Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ORB-SLAM3, Limited resources, Directional consistency check, Multi-objective 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However, on resource-constrained hardware platforms, the real-time performance and robustness of VSLAM systems are severely hindered by limited computational resources, especially in indoor environments where illumination changes and dynamic scenes cause significant fluctuations in feature point quantity, undermining tracking stability and increasing backend optimization overhead. To address these challenges, we propose FCD-VSLAM, a lightweight ORB-SLAM3 algorithm integrated with feature point convergence adaptation and pose-based directional consistency checks. Specifically, we design a Feature-Point Convergence Adaptive Module that combines image enhancement, non-maximum suppression, and multi-objective optimization to dynamically determine the optimal feature point range. A Directional Consistency Check Skip Mechanism based on a motion model is introduced to reduce redundant computations during stable pose changes, and a Descriptor Optimization Method fusing directional information is proposed to improve matching accuracy. Evaluations on the EuRoC public dataset and a private dataset show that FCD-VSLAM outperforms the original ORB-SLAM3 and other lightweight SLAM methods, achieving an average 20.18% improvement in localization accuracy (ATE RMSE) on EuRoC sequences and a 12.45% reduction in per-frame processing time. 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