Enhanced Geodetic Ground-Truth Methodology and Experimental Validation for Mobile Robots: Insights from the VRI4WD UFPR-MAP Dataset

Enhanced Geodetic Ground-Truth Methodology and Experimental Validation for Mobile Robots: Insights from the VRI4WD UFPR-MAP Dataset | 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 Method Article Enhanced Geodetic Ground-Truth Methodology and Experimental Validation for Mobile Robots: Insights from the VRI4WD UFPR-MAP Dataset Carlos Eduardo Magrin, Felipe Gustavo Bombardelli, Wander da Cruz, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8255082/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 article introduces an enhanced geodetic ground-truth methodology tailored for mobile robot localization and mapping in hybrid indoor and outdoor environments. The work details the development of a comprehensive data acquisition system integrated into the VRI4WD wheeled robotic platform, enabling synchronized capture of diverse sensor modalities. A rigorous geodetic ground-truth methodology is presented, leveraging precise topographic surveying with GNSS and total station measurements referenced to standardized geodetic systems, ensuring highly accurate and globally consistent positioning data. A survey and comparison of leading multi-sensor datasets commonly employed as benchmarks is conducted to contextualize the UFPR-MAP dataset's contributions. The Sensor Blend Sets (SBS) method is adopted as a principled approach for selecting and grouping complementary sensors for distinct robotic tasks within the dataset. Validation experiments demonstrate the sensor fusion using wheel encoder odometry and laser scanner data, employing the GMapping SLAM algorithm to produce consistent occupancy grid maps across varying trajectories and environmental conditions. This integrated framework advances the state of multi-sensor datasets by providing an accessible, geodetically referenced resource for benchmarking autonomous navigation algorithms in complex real-world scenarios. Geodetic Ground-Truth Sensor Blend Sets Multi-Sensor Mapping Dataset Wheeled Mobile Robot 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-8255082","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":561785846,"identity":"f00da0e0-25ae-416d-9d49-715357a5edf9","order_by":0,"name":"Carlos Eduardo Magrin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYHACxgMw1gGGCgZ+IM1GUA+SljMMkg0kaWFgbCNCC3/72QcHPvxhiObvX/vwcOG8wxLm0gfYHlfg0SJxJt3g4Mw2htwZN54bHJ657bCEZV8Cu+EZPFoMGNIYDvM2MOQ23DgGZGw7XGdwhoEN5DrcWvifMRzm+cOQOx+sZc5hCcJaJIC28LAx5G443wayjggtEjeeMQD9IpG78QYbUO+xdAnLHsZ2Q3xa+PvTGB98+GOTO+/8MebPPDXWEuY8zMce4tMCswyIEqBOZWAkQgPEvgMwLaNgFIyCUTAKUAEAVERP7nw0CaYAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Parana","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"Eduardo","lastName":"Magrin","suffix":""},{"id":561785847,"identity":"e555f800-a4d9-414e-adda-38fb9bdba83a","order_by":1,"name":"Felipe Gustavo Bombardelli","email":"","orcid":"","institution":"Federal University of Parana","correspondingAuthor":false,"prefix":"","firstName":"Felipe","middleName":"Gustavo","lastName":"Bombardelli","suffix":""},{"id":561785849,"identity":"831126a1-4108-40e2-9cec-25dd7ff5cb34","order_by":2,"name":"Wander da Cruz","email":"","orcid":"","institution":"Federal University of Parana","correspondingAuthor":false,"prefix":"","firstName":"Wander","middleName":"da","lastName":"Cruz","suffix":""},{"id":561785850,"identity":"8a9b1e74-2fa7-46e8-8701-90cff3ee6c2c","order_by":3,"name":"Eduardo Todt","email":"","orcid":"","institution":"Federal University of Parana","correspondingAuthor":false,"prefix":"","firstName":"Eduardo","middleName":"","lastName":"Todt","suffix":""}],"badges":[],"createdAt":"2025-12-02 02:38:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8255082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8255082/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98604593,"identity":"57536696-e003-403d-be5e-4d077787947d","added_by":"auto","created_at":"2025-12-19 13:23:00","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5525,"visible":true,"origin":"","legend":"","description":"","filename":"ba3330f2eef14a8794bbd2547b9a8b90.json","url":"https://assets-eu.researchsquare.com/files/rs-8255082/v1/528fba23b3f19ca950dde1b6.json"},{"id":103508491,"identity":"adde55da-f7a2-457c-affd-150ccb5d1f4e","added_by":"auto","created_at":"2026-02-26 13:50:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":18048755,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerNatureufprMapMagrinBombardelliWanderTodtvDOI.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8255082/v1_covered_193c93d9-4bc9-44c5-9053-90a1f4beefa1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Geodetic Ground-Truth Methodology and Experimental Validation for Mobile Robots: Insights from the VRI4WD UFPR-MAP Dataset","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Geodetic Ground-Truth, Sensor Blend Sets, Multi-Sensor Mapping Dataset, Wheeled Mobile Robot","lastPublishedDoi":"10.21203/rs.3.rs-8255082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8255082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This article introduces an enhanced geodetic ground-truth methodology tailored for mobile robot localization and mapping in hybrid indoor and outdoor environments. The work details the development of a comprehensive data acquisition system integrated into the VRI4WD wheeled robotic platform, enabling synchronized capture of diverse sensor modalities. A rigorous geodetic ground-truth methodology is presented, leveraging precise topographic surveying with GNSS and total station measurements referenced to standardized geodetic systems, ensuring highly accurate and globally consistent positioning data. A survey and comparison of leading multi-sensor datasets commonly employed as benchmarks is conducted to contextualize the UFPR-MAP dataset's contributions. The Sensor Blend Sets (SBS) method is adopted as a principled approach for selecting and grouping complementary sensors for distinct robotic tasks within the dataset. Validation experiments demonstrate the sensor fusion using wheel encoder odometry and laser scanner data, employing the GMapping SLAM algorithm to produce consistent occupancy grid maps across varying trajectories and environmental conditions. This integrated framework advances the state of multi-sensor datasets by providing an accessible, geodetically referenced resource for benchmarking autonomous navigation algorithms in complex real-world scenarios.","manuscriptTitle":"Enhanced Geodetic Ground-Truth Methodology and Experimental Validation for Mobile Robots: Insights from the VRI4WD UFPR-MAP Dataset","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-19 13:22:55","doi":"10.21203/rs.3.rs-8255082/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":"daf19180-e5b0-432b-8de8-3581faae8c8e","owner":[],"postedDate":"December 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-21T16:54:26+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-19 13:22:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8255082","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8255082","identity":"rs-8255082","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}