MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity

MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity | 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 Article MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity Judith Vilella-Cantos, Juan José Cabrera, Luis Payá, Mónica Ballesta, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8273194/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Place recognition is crucial for autonomous navigation, requiring accuracy despite seasonal, weather, or environmental changes. This paper presents our method, MinkUNeXt-SI, which, starting from a LiDAR point cloud, preprocesses the input data to obtain its spherical coordinates and normalized intensity values. It then produces a robust place recognition descriptor. To that end, a deep learning approach that combines Minkowski convolutions and a U-net architecture with skip connections is used. MinkUNeXt-SI’s high recall results of up to 99.15% demonstrate that this method reaches and surpasses state-of-the-art performance while it also generalizes satisfactorily to other datasets. Additionally, we showcase the capture of a custom dataset and its use in evaluating our solution, which also achieves recall results above 85%. These results illustrate that our method improves upon the state-of-the-art by close to +10% in this growing field. Both the code of our solution and the runs of our dataset are publicly available for reproducibility purposes. Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Physical sciences/Mathematics and computing Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 02 Apr, 2026 Reviews received at journal 13 Jan, 2026 Reviewers agreed at journal 12 Dec, 2025 Reviewers invited by journal 12 Dec, 2025 Editor assigned by journal 10 Dec, 2025 Editor invited by journal 08 Dec, 2025 Submission checks completed at journal 05 Dec, 2025 First submitted to journal 05 Dec, 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-8273194","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":559685350,"identity":"8e3a62f5-666b-4786-a79e-5ead33dd3fd8","order_by":0,"name":"Judith 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intensity","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8273194/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8273194/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":" Place recognition is crucial for autonomous navigation, requiring accuracy despite seasonal, weather, or environmental changes. This paper presents our method, MinkUNeXt-SI, which, starting from a LiDAR point cloud, preprocesses the input data to obtain its spherical coordinates and normalized intensity values. It then produces a robust place recognition descriptor. To that end, a deep learning approach that combines Minkowski convolutions and a U-net architecture with skip connections is used. MinkUNeXt-SI’s high recall results of up to 99.15% demonstrate that this method reaches and surpasses state-of-the-art performance while it also generalizes satisfactorily to other datasets. Additionally, we showcase the capture of a custom dataset and its use in evaluating our solution, which also achieves recall results above 85%. These results illustrate that our method improves upon the state-of-the-art by close to +10% in this growing field. Both the code of our solution and the runs of our dataset are publicly available for reproducibility purposes.","manuscriptTitle":"MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 05:24:03","doi":"10.21203/rs.3.rs-8273194/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"213891043710141765166580454898216305285","date":"2026-04-02T08:49:32+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-13T09:07:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"297171959325346168046336374225643053952","date":"2025-12-12T05:47:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-12T05:29:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-10T15:40:39+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-08T22:57:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-05T14:36:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-05T14:31:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1b03aadb-a5b5-4057-a012-35096ce3d5f1","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59589550,"name":"Physical sciences/Engineering"},{"id":59589551,"name":"Earth and environmental sciences/Environmental sciences"},{"id":59589552,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-12-17T05:24:04+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 05:24:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8273194","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8273194","identity":"rs-8273194","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}