{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,18]],"date-time":"2026-05-18T13:20:53Z","timestamp":1779110453002,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2019,8,4]],"date-time":"2019-08-04T00:00:00Z","timestamp":1564876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["No.2018YFB1305001, No.2016YFB0501803"],"award-info":[{"award-number":["No.2018YFB1305001, No.2016YFB0501803"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The fusion of visual and inertial measurements for motion tracking has become prevalent in the robotic community, due to its complementary sensing characteristics, low cost, and small space requirements. This fusion task is known as the vision-aided inertial navigation system problem. We present a novel hybrid sliding window optimizer to achieve information fusion for a tightly-coupled vision-aided inertial navigation system. It possesses the advantages of both the conditioning-based method and the prior-based method. A novel distributed marginalization method was also designed based on the multi-state constraints method with significant efficiency improvement over the traditional method. The performance of the proposed algorithm was evaluated with the publicly available EuRoC datasets and showed competitive results compared with existing algorithms.<\/jats:p>","DOI":"10.3390\/s19153418","type":"journal-article","created":{"date-parts":[[2019,8,5]],"date-time":"2019-08-05T03:25:22Z","timestamp":1564975522000},"page":"3418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Hybrid Sliding Window Optimizer for Tightly-Coupled Vision-Aided Inertial Navigation System"],"prefix":"10.3390","volume":"19","author":[{"given":"Junxiang","family":"Jiang","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5591-0859","authenticated-orcid":false,"given":"Xiaoji","family":"Niu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruonan","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingnan","family":"Liu","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"},{"name":"Collaborative Innovation Center of Geospatial Technology, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kneip, L., Weiss, S., and Siegwart, R. 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