{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T06:39:57Z","timestamp":1781073597901,"version":"3.54.1"},"reference-count":53,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,21]],"date-time":"2025-12-21T00:00:00Z","timestamp":1766275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research and Development and Achievement Transformation Project of Inner Mongolia Autonomous Region","award":["2025YFHH0001-01"],"award-info":[{"award-number":["2025YFHH0001-01"]}]},{"name":"Young Backbone Teachers Support Plan of Beijing Information Science & Technology University","award":["YBT 202405"],"award-info":[{"award-number":["YBT 202405"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>To address the issue of decreased localization accuracy and robustness in existing visual SLAM systems caused by imprecise identification of dynamic regions in complex dynamic scenes\u2014leading to dynamic interference or reduction in valid static feature points, this paper proposes a dynamic visual SLAM method integrating instance-level motion classification, temporally adaptive super-pixel segmentation, and optical flow propagation. The system first employs an instance-level motion classifier combining residual flow estimation and a YOLOv8-seg instance segmentation model to distinguish moving objects. Then, temporally adaptive super-pixel segmentation algorithm SLIC (TA-SLIC) is applied to achieve fine-grained dynamic region partitioning. Subsequently, a proposed dynamic region missed-detection correction mechanism based on optical flow propagation (OFP) is used to refine the missed-detection mask, enabling accurate identification and capture of motion regions containing non-rigid local object movements, undefined moving objects, and low-dynamic objects. Finally, dynamic feature points are removed, and valid static features are utilized for pose estimation. The localization accuracy of the visual SLAM system is validated using two widely adopted datasets, TUM and BONN. Experimental results demonstrate that the proposed method effectively suppresses interference from dynamic objects (particularly non-rigid local motions) and significantly enhances both localization accuracy and system robustness in dynamic environments.<\/jats:p>","DOI":"10.3390\/ijgi15010007","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:22:27Z","timestamp":1766578947000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["TAS-SLAM: A Visual SLAM System for Complex Dynamic Environments Integrating Instance-Level Motion Classification and Temporally Adaptive Super-Pixel Segmentation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2196-2343","authenticated-orcid":false,"given":"Yiming","family":"Li","sequence":"first","affiliation":[{"name":"Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-3418-9230","authenticated-orcid":false,"given":"Liuwei","family":"Lu","sequence":"additional","affiliation":[{"name":"Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guangming","family":"Guo","sequence":"additional","affiliation":[{"name":"Intelligent Equipment Research Institute, Beijing Academy of Science and Technology, Beijing 100061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luying","family":"Na","sequence":"additional","affiliation":[{"name":"Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xianpu","family":"Liang","sequence":"additional","affiliation":[{"name":"Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0005-5445","authenticated-orcid":false,"given":"Peng","family":"Su","sequence":"additional","affiliation":[{"name":"Mechanical Electrical Engineering School, Beijing Information Science & Technology University, Beijing 100192, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"An","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengjiang","family":"Wang","sequence":"additional","affiliation":[{"name":"China Academy of Safety Science and Technology, Beijing 100012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","author":"Engel","year":"2018","journal-title":"IEEE Trans. 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