Autonomous Mobile Vehicle Using ROS2 and 2D-Lidar and SLAM Navigation

Autonomous Mobile Vehicle Using ROS2 and 2D-Lidar and SLAM Navigation | 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 Autonomous Mobile Vehicle Using ROS2 and 2D-Lidar and SLAM Navigation Pratham Gyanani, Mridul Agarwal, Ranjeet Osari, Garima Chandore This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4323431/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 This study describes the use of the SLAM algorithm in autonomous mobile vehicle/robot navigation. The proposed work is organized inside the Robot Operating System (ROS2) architecture. Data visualization is done with Rviz, and vehicle simulation is done with Gazebo. SLAM Toolbox uses laser and odometry data from several sensors for mapping. We have built a small bot which uses Differential Drive Control System. It also uses the Nav2 plugin of ROS2 for navigation from one place to another in the environment by first giving the initial co-ordinates of the robot and then the Goal pose where the robot has to reach. It also implements online asynchronous plugin of ros2 which helps the robot to detect real time change in the map during the navigation so it will change its path if a new object is detected real time Robot Operating System (ROS) Adaptive Monte Carlo Localization (AMCL) SLAM Toolbox Simultaneous localization and mapping (SLAM) URDF Odometry ROS2 Control Rviz Gazebo 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-4323431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":296045111,"identity":"ed98443e-46fb-46df-95b4-700bb2162522","order_by":0,"name":"Pratham Gyanani","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYPCCAwwM7A1A2sCCWB0JQC08B0BaJEjRIpEAYhGhRT4i99iHnz/uyOvOfH51w48CCQb+9u4EvFoMb+Qlz+xJeGa47XZO2c0eoMMkzpzdgF/LjBxjBp6Ew4xALWk3eIBaDCRyCWth/JNw2H7bzTNpN/8Qo0VeIseYGWhL4rYb7MduE2WLAc+7ZGaZtMPJ287ksN2WMZDgIegX+fbcw4xvbA7bbjt+/NnNN39s5PjbewnYciEHxuQxAJN4lYNt6T8DY7I/IKh6FIyCUTAKRiYAAPCzTjYTAVcQAAAAAElFTkSuQmCC","orcid":"","institution":"Medi-Caps University","correspondingAuthor":true,"prefix":"","firstName":"Pratham","middleName":"","lastName":"Gyanani","suffix":""},{"id":296045112,"identity":"aff76043-315e-4d35-840e-bc39751d6533","order_by":1,"name":"Mridul Agarwal","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Mridul","middleName":"","lastName":"Agarwal","suffix":""},{"id":296045113,"identity":"6660f69b-73c3-4e12-bf0f-68e38769b08c","order_by":2,"name":"Ranjeet Osari","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Ranjeet","middleName":"","lastName":"Osari","suffix":""},{"id":296045114,"identity":"e96c452e-1662-4fab-8f0f-e0b7ba82b2d8","order_by":3,"name":"Garima Chandore","email":"","orcid":"","institution":"Medi-Caps University","correspondingAuthor":false,"prefix":"","firstName":"Garima","middleName":"","lastName":"Chandore","suffix":""}],"badges":[],"createdAt":"2024-04-25 10:22:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4323431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4323431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61031755,"identity":"d9e1ab8b-71d1-4eb5-bd6b-554f7b1af67e","added_by":"auto","created_at":"2024-07-24 19:45:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":608717,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchPaperFinalSpringer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4323431/v1_covered_82bd3b7d-4f4c-4916-b061-bba5a6d57501.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Autonomous Mobile Vehicle Using ROS2 and 2D-Lidar and SLAM Navigation","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":"Robot Operating System (ROS), Adaptive Monte Carlo Localization (AMCL), SLAM Toolbox, Simultaneous localization and mapping (SLAM), URDF, Odometry, ROS2 Control, Rviz, Gazebo","lastPublishedDoi":"10.21203/rs.3.rs-4323431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4323431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study describes the use of the SLAM algorithm in autonomous mobile vehicle/robot navigation. 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