{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T13:45:37Z","timestamp":1762868737762,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T00:00:00Z","timestamp":1643241600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Most visual\u2013inertial navigation systems (VINSs) suffer from moving objects and achieve poor positioning accuracy in dynamic environments. Therefore, to improve the positioning accuracy of VINS in dynamic environments, a monocular visual\u2013inertial navigation system, VINS-dimc, is proposed. This system integrates various constraints on the elimination of dynamic feature points, which helps to improve the positioning accuracy of VINSs in dynamic environments. First, the motion model, computed from the inertial measurement unit (IMU) data, is subjected to epipolar constraint and flow vector bound (FVB) constraint to eliminate feature matching that deviates significantly from the motion model. This algorithm then combines multiple feature point matching constraints that avoid the lack of single constraints and make the system more robust and universal. Finally, VINS-dimc was proposed, which can adapt to a dynamic environment. Experiments show that the proposed algorithm could accurately eliminate the dynamic feature points on moving objects while preserving the static feature points. It is a great help for the positioning accuracy and robustness of VINSs, whether they are from self-collected data or public datasets.<\/jats:p>","DOI":"10.3390\/ijgi11020095","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T22:00:56Z","timestamp":1643320856000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["VINS-Dimc: A Visual-Inertial Navigation System for Dynamic Environment Integrating Multiple Constraints"],"prefix":"10.3390","volume":"11","author":[{"given":"Dong","family":"Fu","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1975-4254","authenticated-orcid":false,"given":"Hao","family":"Xia","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yujie","family":"Liu","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanyou","family":"Qiao","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Alliez, P., Bonardi, F., Bouchafa, S., Didier, J.-Y., Hadj-Abdelkader, H., Mu\u00f1oz, F.I.I., Kachurka, V., Rault, B., Robin, M., and Roussel, D. 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