{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:39:18Z","timestamp":1760150358416,"version":"build-2065373602"},"reference-count":42,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2023,11,19]],"date-time":"2023-11-19T00:00:00Z","timestamp":1700352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation","award":["61873353","61602529"],"award-info":[{"award-number":["61873353","61602529"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Currently, in most traditional VSLAM (visual SLAM) systems, static assumptions result in a low accuracy in dynamic environments, or result in a new and higher level of accuracy but at the cost of sacrificing the real\u2013time property. In highly dynamic scenes, balancing a high accuracy and a low computational cost has become a pivotal requirement for VSLAM systems. This paper proposes a new VSLAM system, balancing the competitive demands between positioning accuracy and computational complexity and thereby further improving the overall system properties. From the perspective of accuracy, the system applies an improved lightweight target detection network to quickly detect dynamic feature points while extracting feature points at the front end of the system, and only feature points of static targets are applied for frame matching. Meanwhile, the attention mechanism is integrated into the target detection network to continuously and accurately capture dynamic factors to cope with more complex dynamic environments. From the perspective of computational expense, the lightweight network Ghostnet module is applied as the backbone network of the target detection network YOLOv5s, significantly reducing the number of model parameters and improving the overall inference speed of the algorithm. Experimental results on the TUM dynamic dataset indicate that in contrast with the ORB\u2013SLAM3 system, the pose estimation accuracy of the system improved by 84.04%. In contrast with dynamic SLAM systems such as DS\u2013SLAM and DVO SLAM, the system has a significantly improved positioning accuracy. In contrast with other VSLAM algorithms based on deep learning, the system has superior real\u2013time properties while maintaining a similar accuracy index.<\/jats:p>","DOI":"10.3390\/s23229274","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T01:54:12Z","timestamp":1700445252000},"page":"9274","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A Lightweight Visual Simultaneous Localization and Mapping Method with a High Precision in Dynamic Scenes"],"prefix":"10.3390","volume":"23","author":[{"given":"Qi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wentao","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weirong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuan","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer and Information Engineering, Central South University of Forestry and Technology, Changsha 410018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s41074-017-0027-2","article-title":"VSLAM algorithms: A survey from 2010 to 2016","volume":"9","author":"Taketomi","year":"2017","journal-title":"IPSJ Trans. 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