{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,29]],"date-time":"2026-03-29T06:01:56Z","timestamp":1774764116585,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T00:00:00Z","timestamp":1510617600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Science Fund of China","award":["6171136"],"award-info":[{"award-number":["6171136"]}]},{"name":"Doctoral Program of Higher Education of China","award":["20110142110069"],"award-info":[{"award-number":["20110142110069"]}]},{"name":"CALT Aerospace Fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we propose a new visual-inertial Simultaneous Localization and Mapping (SLAM) algorithm. With the tightly coupled sensor fusion of a global shutter monocular camera and a low-cost Inertial Measurement Unit (IMU), this algorithm is able to achieve robust and real-time estimates of the sensor poses in unknown environment. To address the real-time visual-inertial fusion problem, we present a parallel framework with a novel IMU initialization method. Our algorithm also benefits from the novel IMU factor, the continuous preintegration method, the vision factor of directional error, the separability trick and the robust initialization criterion which can efficiently output reliable estimates in real-time on modern Central Processing Unit (CPU). Tremendous experiments also validate the proposed algorithm and prove it is comparable to the state-of-art method.<\/jats:p>","DOI":"10.3390\/s17112613","type":"journal-article","created":{"date-parts":[[2017,11,14]],"date-time":"2017-11-14T10:58:32Z","timestamp":1510657112000},"page":"2613","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Monocular Visual-Inertial SLAM: Continuous Preintegration and Reliable Initialization"],"prefix":"10.3390","volume":"17","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhong","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenjuan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beijing Aerospace Automatic Control Institute, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Aerospace Automatic Control Institute, Beijing 100854, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianguo","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Multi-Spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bachrach, A., de Winter, A., He, R., Hemann, G., Prentice, S., and Roy, N. 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