{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,5]],"date-time":"2026-07-05T10:33:55Z","timestamp":1783247635795,"version":"3.54.6"},"reference-count":22,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,19]],"date-time":"2020-01-19T00:00:00Z","timestamp":1579392000000},"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>A visual\u2013inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model. The output of the attitude information from the visual\u2013inertial odometer was used as the input of the conditional random field model. The feature function between the attitude information and the expected value was established, and the maximum probabilistic value of the attitude was estimated. Finally, the closed-loop feedback correction of the visual\u2013inertial system was carried out with the probabilistic attitude value. A number of experiments were designed to verify the feasibility and reliability of the positioning method proposed in this paper.<\/jats:p>","DOI":"10.3390\/s20020552","type":"journal-article","created":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T03:04:43Z","timestamp":1579575883000},"page":"552","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Improving Positioning Accuracy via Map Matching Algorithm for Visual\u2013Inertial Odometer"],"prefix":"10.3390","volume":"20","author":[{"given":"Juan","family":"Meng","sequence":"first","affiliation":[{"name":"College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4058-4170","authenticated-orcid":false,"given":"Mingrong","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pu","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jitong","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuman","family":"Mou","sequence":"additional","affiliation":[{"name":"College of Automation, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China"},{"name":"Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China"},{"name":"Beijing Key Laboratory of Computational Intelligence and Intelligent Systems, Beijing 100124, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, H., Li, T., Yin, L., Liu, D., Zhou, Y., Zhang, J., and Pan, F. 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