Enhancing Loop Closure Detection with Object Semantic Scan Context

Enhancing Loop Closure Detection with Object Semantic Scan Context | 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 Enhancing Loop Closure Detection with Object Semantic Scan Context Dhruv Kumarjiguda, Saurab Verma, Rajdeep Dutta, Syed Zeeshan Ahmed, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8727894/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Loop closure allows a mobile robot to rectify the accumulated drift in its estimated position, facilitating in building an accurate map of the surrounding environment by recognizing previously visited places. 3D point cloud-based loop closure is an essential task for the growing mobile robotics sector.State-of-the-art (SOTA) methods require a robot to revisit a previously explored area in close proximity.To reduce unnecessary robot traversals in the environment, we construct an efficient descriptor, dubbed as Object Semantic Scan Context (OSSC), by encoding semantic features in local descriptors (representations) around external references, i.e., Main Objects (MOs), for accurate loop closure detection even between distant lidar scans. Moreover, we adopt effective strategies for selecting MO(s) and weighting semantic labels to enhance the discriminative power of OSSC in challenging scenarios.Rather than relying on semantic sparsity, OSSC captures the semantic patterns of all objects around MO(s).The proposed descriptor is extensively tested on the \hl{SemanticKITTI and RELLIS-3D} datasets andthe achieved high accuracy in a variety of scenarios, especially with spatially distant scans, corroborate its efficiency and robustness. Loop closure Point cloud Scan context Place recognition Localization SLAM Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 Apr, 2026 Reviewers agreed at journal 25 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 18 Mar, 2026 Editor assigned by journal 15 Mar, 2026 Submission checks completed at journal 30 Jan, 2026 First submitted to journal 29 Jan, 2026 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-8727894","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609834037,"identity":"19a2b4ec-aa75-43a2-9d4b-97e4b5642515","order_by":0,"name":"Dhruv Kumarjiguda","email":"","orcid":"","institution":"Nanyang Technological University","correspondingAuthor":false,"prefix":"","firstName":"Dhruv","middleName":"","lastName":"Kumarjiguda","suffix":""},{"id":609834038,"identity":"45900700-defe-4e16-a560-c63ae11c56f6","order_by":1,"name":"Saurab Verma","email":"","orcid":"","institution":"Institute for Infocomm 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