{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T01:38:34Z","timestamp":1791250714679,"version":"4.1.0"},"reference-count":22,"publisher":"Optica Publishing Group","issue":"16","license":[{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/doi.org\/10.1364\/OA_License_v2#VOR"},{"start":{"date-parts":[[2026,8,3]],"date-time":"2026-08-03T00:00:00Z","timestamp":1785715200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/opg.optica.org\/policies\/opg-tdm-policy.json"}],"funder":[{"name":"National High-Level Personnel of Special Support"},{"name":"Basic Research Program of Jiangsu","award":["BK20252002"],"award-info":[{"award-number":["BK20252002"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61960206005"],"award-info":[{"award-number":["61960206005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61871111"],"award-info":[{"award-number":["61871111"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"},{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Project of the National Mobile Communications Research Laboratory","award":["2026A03"],"award-info":[{"award-number":["2026A03"]}]}],"content-domain":{"domain":["opg.optica.org"],"crossmark-restriction":false},"short-container-title":["Opt. Lett."],"published-print":{"date-parts":[[2026,8,15]]},"abstract":"<jats:p>Accurate six-degree-of-freedom (6-DoF) pose sensing is essential for autonomous systems, intelligent robotics, and the Internet of Things. Conventional pose-sensing approaches often rely on bulky imaging modules and complex visual pipelines, limiting their deployment on compact and embedded platforms. Metalens-integrated cameras provide an ultrathin optical front end, but their reduced contrast and imaging blur make pose estimation unstable. Here, we demonstrate 6-DoF pose sensing and dynamic tracking using a metalens-integrated camera assisted by an ArUco marker tailored to its imaging characteristics. Robust four-corner correspondences are directly extracted from metalens images, and a calibrated planar PnP solver enables accurate pose estimation. Static measurements yield mean absolute translation errors of 1.43, 1.08, and 4.71\u2009mm along the x, y, and z axes, respectively, with mean yaw, pitch, and roll errors of 1.41\u00b0, 6.37\u00b0, and 9.06\u00b0. Dynamic experiments further show stable continuous marker tracking, highlighting the potential of this compact localization framework for embedded pose-aware sensing.<\/jats:p>","DOI":"10.1364\/ol.610051","type":"journal-article","created":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T14:00:11Z","timestamp":1785160811000},"page":"4472","update-policy":"https:\/\/doi.org\/10.1364\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Accurate 6-DoF pose sensing and dynamic tracking enabled by a metalens-integrated camera"],"prefix":"10.1364","volume":"51","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9376-6077","authenticated-orcid":true,"given":"Ji","family":"Chen","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04ct4d772","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Purple Mountain Laboratories"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifan","family":"Fei","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04ct4d772","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongyi","family":"Yuan","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04ct4d772","id-type":"ROR","asserted-by":"publisher"}]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zaichen","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04ct4d772","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Purple Mountain Laboratories"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"285","published-online":{"date-parts":[[2026,8,3]]},"reference":[{"key":"ol-51-16-4472-R1","author":"Hartley","year":"2004"},{"key":"ol-51-16-4472-R2","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11263-008-0152-6","type":"journal-article","volume":"81","author":"Lepetit","year":"2009","journal-title":"Int. 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All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.","name":"copyright","label":"Copyright"}]}}