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This paper proposes ULG-SLAM, a novel unsupervised learning and geometric-based visual SLAM algorithm for robot localizability estimation to improve the accuracy and robustness of visual SLAM. Firstly, a dynamic feature filtering based on unsupervised learning and moving consistency checks is developed to eliminate the features of dynamic objects. Secondly, an improved line feature extraction algorithm based on LSD is proposed to optimize the effect of geometric feature extraction. Thirdly, geometric features are used to optimize localizability estimation, and an adaptive weight model and attention mechanism are built using the method of region delimitation and region growth. Finally, to verify the effectiveness and robustness of localizability estimation, multiple indoor experiments using the EuRoC dataset and TUM RGB-D dataset are conducted. Compared with ORBSLAM2, the experimental results demonstrate that absolute trajectory accuracy can be improved by 95% for equivalent processing speed in walking sequences. In fr3\/walking_xyz and fr3\/walking_half, ULG-SLAM tracks more trajectories than DS-SLAM, and the ATE RMSE is improved by 36% and 6%, respectively. Furthermore, the improvement in robot localizability over DynaSLAM is noteworthy, coming in at about 11% and 3%, respectively.<\/jats:p>","DOI":"10.3390\/rs16111968","type":"journal-article","created":{"date-parts":[[2024,5,30]],"date-time":"2024-05-30T06:08:14Z","timestamp":1717049294000},"page":"1968","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["ULG-SLAM: A Novel Unsupervised Learning and Geometric Feature-Based Visual SLAM Algorithm for Robot Localizability Estimation"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5136-8416","authenticated-orcid":false,"given":"Yihan","family":"Huang","sequence":"first","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5426-8390","authenticated-orcid":false,"given":"Fei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"The College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhilin","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electrical and Automation Engineering, Nanjing Normal University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Zhao","sequence":"additional","affiliation":[{"name":"The State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310058, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xixiang","family":"Liu","sequence":"additional","affiliation":[{"name":"The College of Instrument Science & Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"30739","DOI":"10.1109\/JSEN.2023.3328715","article-title":"An Indoor UWB NLOS Correction Positioning Method Based on Anchor LOS\/NLOS Map","volume":"23","author":"Wang","year":"2023","journal-title":"IEEE Sens. 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