{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:04:33Z","timestamp":1784390673349,"version":"3.55.0"},"reference-count":37,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,18]],"date-time":"2024-02-18T00:00:00Z","timestamp":1708214400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"publisher","award":["62173192"],"award-info":[{"award-number":["62173192"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation","doi-asserted-by":"publisher","award":["JCYJ20220530162202005"],"award-info":[{"award-number":["JCYJ20220530162202005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016804","name":"Shenzhen Natural Science Foundation","doi-asserted-by":"publisher","award":["62173192"],"award-info":[{"award-number":["62173192"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016804","name":"Shenzhen Natural Science Foundation","doi-asserted-by":"publisher","award":["JCYJ20220530162202005"],"award-info":[{"award-number":["JCYJ20220530162202005"]}],"id":[{"id":"10.13039\/100016804","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Visual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases. In this paper, we propose a novel lightweight point\u2013line visual\u2013inertial odometry algorithm to solve this problem, called LRPL-VIO. Firstly, a fast line matching method is proposed based on the assumption that the photometric values of endpoints and midpoints are invariant between consecutive frames, which greatly reduces the time consumption of the front end. Then, an efficient filter-based state estimation framework is designed to finish information fusion (point, line, and inertial). Fresh measurements of line features with good tracking quality are selected for state estimation using a unique feature selection scheme, which improves the efficiency of the proposed algorithm. Finally, validation experiments are conducted on public datasets and in real-world tests to evaluate the performance of LRPL-VIO and the results show that we outperform other state-of-the-art algorithms especially in terms of speed and robustness.<\/jats:p>","DOI":"10.3390\/s24041322","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T03:18:38Z","timestamp":1708312718000},"page":"1322","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["LRPL-VIO: A Lightweight and Robust Visual\u2013Inertial Odometry with Point and Line Features"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4822-2265","authenticated-orcid":false,"given":"Feixiang","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6804-3865","authenticated-orcid":false,"given":"Lu","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7170-7718","authenticated-orcid":false,"given":"Wanbiao","family":"Lin","sequence":"additional","affiliation":[{"name":"Shenzhen Research Institute, Nankai University, Shenzhen 518081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-0061-3390","authenticated-orcid":false,"given":"Jingyang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-9012","authenticated-orcid":false,"given":"Lei","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Artificial Intelligence, Nankai University, Tianjin 300350, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tourani, A., Bavle, H., Sanchez-Lopez, J.L., and Voos, H. 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