An accurate and robust RGB-D visual SLAM method in dynamic environments

An accurate and robust RGB-D visual SLAM method in dynamic 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 An accurate and robust RGB-D visual SLAM method in dynamic environments Zhen Li, Xinguang Zhang, Lihao Fan, Jiahao Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5311384/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 Traditional SLAM systems have achieved good robustness in static environments. However, the presence of dynamic objects in real-world environments can significantly reduce their localization accuracy. This paper introduces a dynamic SLAM system that combines semantic segmentation with epipolar constraints. The system can detect and remove dynamic points in dynamic environments, achieving good localization performance and generating dense point cloud maps. Initially, the system employs the YOLOv5 deep learning network to extract semantic information from images, thereby generating prior semantic masks for dynamic objects. Subsequently, a novel method for eliminating dynamic feature points is introduced. This method utilizes an adaptive threshold correlated with depth, integrating semantic prior information and epipolar constraint geometric information to further assess the motion state of feature points, thereby removing dynamic points. Finally, a dense point cloud map is produced in dynamic environments by integrating depth information with semantic information. Experiments conducted on the TUM dataset indicate that, compared to ORBSLAM2, the proposed system achieves an average localization accuracy improvement of 95% in highly dynamic sequences, demonstrating the algorithm's effectiveness in enhancing localization accuracy in dynamic environments. deep learning epipolar constraint dense mapping Visual SLAM 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-5311384","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":369058879,"identity":"916b8ed5-d421-4e24-ab58-556d963f8cdf","order_by":0,"name":"Zhen Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Li","suffix":""},{"id":369058880,"identity":"e6d44e68-a430-40f5-a7b7-4cc6946da896","order_by":1,"name":"Xinguang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYJACg4QKGx5+9gYStBQ8OJMmI9lzgAQtHx+2HLYxuOFApHL5GekXNyQ2nOdhuMHA+OFjDhFaGHvOFBsk7rjNwzi7gVly5jYitDCz96QZJJ65zcMsc4CNmZcYLWzMPOk/EtvO8bBJJBCphYe9/YBBYtsBHh6itUjwnAHGy5lkHgmeg83E+QUYYg8Mf1TY2dsfbz744SMxWoBOM4AyGBuIUg8E7A+IVTkKRsEoGAUjFQAAv/w2rsLw5U0AAAAASUVORK5CYII=","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Xinguang","middleName":"","lastName":"Zhang","suffix":""},{"id":369058881,"identity":"a7c7c96d-1226-4a2a-a732-8a9ad822fe23","order_by":2,"name":"Lihao Fan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lihao","middleName":"","lastName":"Fan","suffix":""},{"id":369058882,"identity":"843734bb-077a-48aa-aeac-4eb3d6b1086c","order_by":3,"name":"Jiahao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jiahao","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-10-22 11:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5311384/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5311384/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67478920,"identity":"4c075e61-6ddd-48ea-bfd5-e4176adfad2b","added_by":"auto","created_at":"2024-10-25 13:02:22","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4565595,"visible":true,"origin":"","legend":"","description":"","filename":"AnaccurateandrobustRGBDvisualSLAMmethodindynamicenvironments1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5311384/v1_covered_39b9ceea-d830-47ba-b052-75e9f23092d9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An accurate and robust RGB-D visual SLAM method in dynamic 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":"deep learning, epipolar constraint, dense mapping, Visual SLAM","lastPublishedDoi":"10.21203/rs.3.rs-5311384/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5311384/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Traditional SLAM systems have achieved good robustness in static environments. However, the presence of dynamic objects in real-world environments can significantly reduce their localization accuracy. This paper introduces a dynamic SLAM system that combines semantic segmentation with epipolar constraints. The system can detect and remove dynamic points in dynamic environments, achieving good localization performance and generating dense point cloud maps. Initially, the system employs the YOLOv5 deep learning network to extract semantic information from images, thereby generating prior semantic masks for dynamic objects. Subsequently, a novel method for eliminating dynamic feature points is introduced. This method utilizes an adaptive threshold correlated with depth, integrating semantic prior information and epipolar constraint geometric information to further assess the motion state of feature points, thereby removing dynamic points. Finally, a dense point cloud map is produced in dynamic environments by integrating depth information with semantic information. Experiments conducted on the TUM dataset indicate that, compared to ORBSLAM2, the proposed system achieves an average localization accuracy improvement of 95\\% in highly dynamic sequences, demonstrating the algorithm's effectiveness in enhancing localization accuracy in dynamic environments.","manuscriptTitle":"An accurate and robust RGB-D visual SLAM method in dynamic environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-24 07:54:28","doi":"10.21203/rs.3.rs-5311384/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"8b2aae72-3209-4425-94dd-1ef740062d7e","owner":[],"postedDate":"October 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-25T12:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-24 07:54:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5311384","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5311384","identity":"rs-5311384","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}