{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T21:57:11Z","timestamp":1772315831165,"version":"3.50.1"},"reference-count":12,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,22]],"date-time":"2017-10-22T00:00:00Z","timestamp":1508630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In 2015, HTC and Valve launched a virtual reality headset empowered with Lighthouse, the cutting-edge space positioning technology. Although Lighthouse is superior in terms of accuracy, latency and refresh rate, its algorithms do not support base station expansion, and is flawed concerning occlusion in moving targets, that is, it is unable to calculate their poses with a small set of sensors, resulting in the loss of optical tracking data. In view of these problems, this paper proposes an improved pose estimation algorithm for cases where occlusion is involved. Our algorithm calculates the pose of a given object with a unified dataset comprising of inputs from sensors recognized by all base stations, as long as three or more sensors detect a signal in total, no matter from which base station. To verify our algorithm, HTC official base stations and autonomous developed receivers are used for prototyping. The experiment result shows that our pose calculation algorithm can achieve precise positioning when a few sensors detect the signal.<\/jats:p>","DOI":"10.3390\/s17102411","type":"journal-article","created":{"date-parts":[[2017,10,23]],"date-time":"2017-10-23T04:32:19Z","timestamp":1508733139000},"page":"2411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An Improved Method of Pose Estimation for Lighthouse Base Station Extension"],"prefix":"10.3390","volume":"17","author":[{"given":"Yi","family":"Yang","sequence":"first","affiliation":[{"name":"School of Optoelectronics, Beijing Institute of Technology (BIT) No. 5 Yard, Zhongguancun South Street Haidian District, Beijing 100081, China"}]},{"given":"Dongdong","family":"Weng","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Beijing Institute of Technology (BIT) No. 5 Yard, Zhongguancun South Street Haidian District, Beijing 100081, China"}]},{"given":"Dong","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Software Chinese Academy of Sciences, No. 4 South Fourth Street, Zhongguancun, Haidian District, Beijing 100190, China"}]},{"given":"Hang","family":"Xun","sequence":"additional","affiliation":[{"name":"School of Optoelectronics, Beijing Institute of Technology (BIT) No. 5 Yard, Zhongguancun South Street Haidian District, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Andrew, Z. 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Intell."},{"key":"ref_5","first-page":"335","article-title":"Model-Based Object Pose in 25 Lines of Code","volume":"Volume 15","author":"Dementhon","year":"2008","journal-title":"Computer Vision\u2014ECCV 2008, Proceedings of the European Conference on Computer Vision, Marseille, France, 12\u201318 October 2008"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1023\/B:VISI.0000025800.10423.1f","article-title":"SoftPOSIT: Simultaneous Pose and Correspondence Determination","volume":"59","author":"David","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","article-title":"EP n P: An Accurate O(n) Solution to the P n P Problem","volume":"81","author":"Lepetit","year":"2008","journal-title":"Int. J. Comput. 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Intell."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TPAMI.2003.1195992","article-title":"Linear Pose Estimation from Points or Lines","volume":"25","author":"Ansar","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","first-page":"2938","article-title":"PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization","volume":"31","author":"Kendall","year":"2016","journal-title":"Educ. Inf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:48:08Z","timestamp":1760208488000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,22]]},"references-count":12,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["s17102411"],"URL":"https:\/\/doi.org\/10.3390\/s17102411","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,22]]}}}