Research on Train Wheel Point Cloud Registration Algorithm Based on Key Points by Fusing Super-4PCS and ICP

Research on Train Wheel Point Cloud Registration Algorithm Based on Key Points by Fusing Super-4PCS and ICP | 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 Research on Train Wheel Point Cloud Registration Algorithm Based on Key Points by Fusing Super-4PCS and ICP Qian Xiao, Xueshan Gao, Zhi Zhang, Yanjie Zhang, Zhongxu Xu, Kaizhi Shi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7320528/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Wheels are critical components of railway vehicles, and the dynamic measurement of wheel parameters is of paramount importance for the safe operation of trains.To enhance the matching accuracy in the existing dynamic measurement processes for train wheel parameters, this paper proposes an improved point cloud registration algorithm based on key point fusion of the Super-4PCS(Super Four-Points Congruent Sets) and ICP (Iterative Closest Point) algorithm. Firstly, point cloud filtering and normal estimation are performed on the wheel point cloud data to obtain source and target point clouds with normal information. Subsequently, the ISS algorithm is employed to extract key points, and the FPFH(Fast Point Feature Histograms) point cloud feature descriptor is utilized to characterize the extracted key points. Then, a two-level registration strategy is used to improve registration accuracy, in which the Super-4PCS algorithm is applied for primary coarse registration and the ICP algorithm is used for the secondary fine registration, respectively. Finally, the experiment is conducted to validate the proposed algorithm and the performance of the algorithm is further comparative analyzed through the listed registration evaluation metrics. Experimental results demonstrate that the proposed algorithm exhibits significant improvements in registration accuracy and robustness for wheel point cloud data compared to the traditional algorithms. Physical sciences/Engineering Physical sciences/Mathematics and computing Train wheel Point cloud registration ISS Super-4PCS ICP Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 01 Sep, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Aug, 2025 Reviews received at journal 21 Aug, 2025 Reviewers agreed at journal 20 Aug, 2025 Reviews received at journal 16 Aug, 2025 Reviewers agreed at journal 16 Aug, 2025 Reviewers invited by journal 13 Aug, 2025 Editor assigned by journal 13 Aug, 2025 Editor invited by journal 11 Aug, 2025 Submission checks completed at journal 09 Aug, 2025 First submitted to journal 09 Aug, 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. 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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-7320528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":503020958,"identity":"b462b5a8-962c-4e38-92c8-225d19e33559","order_by":0,"name":"Qian Xiao","email":"","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Qian","middleName":"","lastName":"Xiao","suffix":""},{"id":503020959,"identity":"32e5e8b2-4810-497b-9701-5c10890f76ef","order_by":1,"name":"Xueshan Gao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYDADAwYGxgcVIBYzcwPRWpgNzoC1MBKvhU0CrIWBgBZ598MHmHnbbOzN2c8eqzhQcTiavx2o5UfFNpxaDM+kJTDObEtL3NmTl3bjwJm03BmHGRsYe87cxq2lIceA4WPb4QSDAzlmtz+22eQ2ALUwM7bh0dL/xoAhse2/vcH5N2YFB9skcucT0iIvAbblAOOGGzlmDAeBtmwgpMVA4lnCwRnnkhM33HhjLAHyy0agloP4/CLfn3zwMU+ZHdBhOYYfgCGWO+/84YMPflTgseUAA8MBDFFMEWRbGvDJjoJRMApGwSgAAQBdJV9Ss7bSYQAAAABJRU5ErkJggg==","orcid":"","institution":"East China Jiaotong University","correspondingAuthor":true,"prefix":"","firstName":"Xueshan","middleName":"","lastName":"Gao","suffix":""},{"id":503020960,"identity":"2ce3b8e9-7f4f-4c7d-912c-6c0c148459c6","order_by":2,"name":"Zhi Zhang","email":"","orcid":"","institution":"China Railway Nanchang Group Co. 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