{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:57:01Z","timestamp":1777705021608,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,12,2]]},"abstract":"<jats:p>Traditional Simultaneous Localization and Mapping application in dynamic situations is constrained by static assumptions. However, the majority of well-known dynamic SLAM systems use deep learning to identify dynamic objects, which creates the issue of trade-offs between accuracy and real-time. To tackle this issue, this work suggests a unique dynamic semantics method(DYS-SLAM) for semantic simultaneous localization and mapping that strikes a trade-off between high accuracy and high real-time performance. We propose M-LK, an enhanced Lucas-Kanade(LK) optical flow method. This technique keeps the continuous motion and greyscale consistency assumptions from the original method while switching out the spatial consistency assumption for a motion consistency assumption, reducing sensitivity to image gradients to identify dynamic feature points and regions efficiently. In order to increase segmentation accuracy while maintaining real-time performance, we develop a segmentation refinement scheme that projects 3D point cloud segmentation results into 2D object detection zones. A dense semantic octree graph is built in the interim to expedite the high-level process. Compared to the four equivalent dynamic SLAM approaches, experiments on the publicly available TUM RGB-D dataset demonstrate that the DYS-SLAM method offers competitive localization accuracy and satisfactory real-time performance in both high and low-dynamic scenarios.<\/jats:p>","DOI":"10.3233\/jifs-234235","type":"journal-article","created":{"date-parts":[[2023,9,22]],"date-time":"2023-09-22T11:41:44Z","timestamp":1695382904000},"page":"10349-10367","source":"Crossref","is-referenced-by-count":6,"title":["DYS-SLAM: A real-time RGBD SLAM combined with optical flow and semantic information in a dynamic environment1"],"prefix":"10.1177","volume":"45","author":[{"given":"Yuhua","family":"Fang","sequence":"first","affiliation":[{"name":"School of Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijun","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kewei","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roozbeh","family":"Zarei","sequence":"additional","affiliation":[{"name":"School of Information Technology, Deakin University, Melbourne, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuntao","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineer, The University of New South Wales, Sydney, 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