Probabilistic Regression for Autonomous Terrain Relative Navigation via Multi-Modal Feature Learning

Probabilistic Regression for Autonomous Terrain Relative Navigation via Multi-Modal Feature Learning | 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 Probabilistic Regression for Autonomous Terrain Relative Navigation via Multi-Modal Feature Learning Ickbum Kim, Abigail Rolen, Sandeep Singh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4270655/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 The extension of human spaceflight across an ever-expanding domain, in conjunction with intricate mission architectures demands a paradigm shift in autonomous navigation algorithms, especially for the powered descent phase of planetary landing. Deep learning architectures have previously been explored to perform low-dimensional localization with limited success. Due to the expectations regarding novel algorithms in the context of real missions, the proposed approaches must be rigorously evaluated in extraneous scenarios and demonstrate sufficient robustness. In the current work, a novel formulation is proposed to train CNN-based deep learning (DL) models in a multi-layer cascading architecture and utilize the resulting classification probabilities as regression weights to estimate the two-dimensional position of the lander spacecraft. The approach leverages image intensity as well as embedded depth information to effectively determine the location of a spacecraft relative to the observed terrain. The efficacy of the proposed DL architecture and the subsequent state-estimation framework is demonstrated using several simulated scenarios and shows promise. convolutional neural network (CNN) cascading architecture machine learning (ML) image processing 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-4270655","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":291957567,"identity":"fcbe73db-2651-4a64-9133-551f5c0e443f","order_by":0,"name":"Ickbum Kim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApklEQVRIiWNgGAWjYBACCWYGhsN/KhgY2EAcorUc4DlDkhYQwduGxCEIJNu5Ew9IzruXx8fAfPA2DzFapJl5Nxww3FZczMbAlmxNlBY5kJbEbQmJbQw8ZtLEazk4B6SF/xtxWkAOO9jYALaFjTgtks28Gw4zHEsoZmNmM7acQ4wWifNnN39mqEnIk29vfnjjDTFaYCCBgZkU5RAto2AUjIJRMApwAQB71iqc3FChrgAAAABJRU5ErkJggg==","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":true,"prefix":"","firstName":"Ickbum","middleName":"","lastName":"Kim","suffix":""},{"id":291957568,"identity":"dc5c4311-cf62-4d8e-b200-e6cb41206bfc","order_by":1,"name":"Abigail Rolen","email":"","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Abigail","middleName":"","lastName":"Rolen","suffix":""},{"id":291957569,"identity":"cdd7933b-7e4c-41a1-aa59-5af3331435cd","order_by":2,"name":"Sandeep Singh","email":"","orcid":"","institution":"Rensselaer Polytechnic Institute","correspondingAuthor":false,"prefix":"","firstName":"Sandeep","middleName":"","lastName":"Singh","suffix":""}],"badges":[],"createdAt":"2024-04-15 15:22:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4270655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4270655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":62905171,"identity":"b3656758-0742-4d91-bf6e-2cbc1892d844","added_by":"auto","created_at":"2024-08-21 01:02:23","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1093629,"visible":true,"origin":"","legend":"","description":"","filename":"CNNCascadingExtendedJournalVersioncorrecttemplate.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4270655/v1_covered_8006ec44-70a8-4840-9383-f0a69e5c3448.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probabilistic Regression for Autonomous Terrain Relative Navigation via Multi-Modal Feature Learning","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":"convolutional neural network (CNN), cascading architecture, machine learning (ML), image processing","lastPublishedDoi":"10.21203/rs.3.rs-4270655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4270655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The extension of human spaceflight across an ever-expanding domain, in conjunction with intricate mission architectures demands a paradigm shift in autonomous navigation algorithms, especially for the powered descent phase of planetary landing. 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