{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:42:32Z","timestamp":1776105752158,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T00:00:00Z","timestamp":1711324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Government Guides Local Science and Technology Development Foundation Projects","award":["ZY19183003"],"award-info":[{"award-number":["ZY19183003"]}]},{"name":"Central Government Guides Local Science and Technology Development Foundation Projects","award":["AB20058001"],"award-info":[{"award-number":["AB20058001"]}]},{"name":"Guangxi Key Research and Development Project","award":["ZY19183003"],"award-info":[{"award-number":["ZY19183003"]}]},{"name":"Guangxi Key Research and Development Project","award":["AB20058001"],"award-info":[{"award-number":["AB20058001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Simultaneous localisation and mapping (SLAM) is crucial in mobile robotics. Most visual SLAM systems assume that the environment is static. However, in real life, there are many dynamic objects, which affect the accuracy and robustness of these systems. To improve the performance of visual SLAM systems, this study proposes a dynamic visual SLAM (SEG-SLAM) system based on the orientated FAST and rotated BRIEF (ORB)-SLAM3 framework and you only look once (YOLO)v5 deep-learning method. First, based on the ORB-SLAM3 framework, the YOLOv5 deep-learning method is used to construct a fusion module for target detection and semantic segmentation. This module can effectively identify and extract prior information for obviously and potentially dynamic objects. Second, differentiated dynamic feature point rejection strategies are developed for different dynamic objects using the prior information, depth information, and epipolar geometry method. Thus, the localisation and mapping accuracy of the SEG-SLAM system is improved. Finally, the rejection results are fused with the depth information, and a static dense 3D mapping without dynamic objects is constructed using the Point Cloud Library. The SEG-SLAM system is evaluated using public TUM datasets and real-world scenarios. The proposed method is more accurate and robust than current dynamic visual SLAM algorithms.<\/jats:p>","DOI":"10.3390\/s24072102","type":"journal-article","created":{"date-parts":[[2024,3,25]],"date-time":"2024-03-25T12:32:36Z","timestamp":1711369956000},"page":"2102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["SEG-SLAM: Dynamic Indoor RGB-D Visual SLAM Integrating Geometric and YOLOv5-Based Semantic Information"],"prefix":"10.3390","volume":"24","author":[{"given":"Peichao","family":"Cong","sequence":"first","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"given":"Jiaxing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"given":"Junjie","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0461-9374","authenticated-orcid":false,"given":"Yixuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China"}]},{"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, W., Shang, G., Jim, A., Zhou, C., Wang, X., Xu, C., Li, Z., and Hu, K. (2022). An Overview on Visual SLAM: From Tradition to Semantic. Remote Sens., 14.","DOI":"10.3390\/rs14133010"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous Localization and Mapping: Part I","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Gupta, A., and Fernando, X. (2022). Simultaneous Localization and Mapping (SLAM) and Data Fusion in Unmanned Aerial Vehicles: Recent Advances and Challenges. Drones, 6.","DOI":"10.32920\/21476628.v1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"012016","DOI":"10.1088\/1742-6596\/2649\/1\/012016","article-title":"Current status and analysis of the development of SLAM technology applied to mobile robots","volume":"2649","author":"Qiu","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Xu, X., Zhang, L., Yang, J., Cao, C., Wang, W., Ran, Y., Tan, Z., and Luo, M. (2022). A Review of Multi-Sensor Fusion SLAM Systems Based on 3D LIDAR. Remote Sens., 14.","DOI":"10.3390\/rs14122835"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Barros, A., Michel, M., Moline, Y., Corre, G., and Carrel, F. (2022). A Comprehensive Survey of Visual SLAM Algorithms. Robotics, 11.","DOI":"10.3390\/robotics11010024"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s10846-023-01812-7","article-title":"Comparison of Modern Open-source Visual SLAM Approaches","volume":"107","author":"Sharafutdinov","year":"2023","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1147","DOI":"10.1109\/TRO.2015.2463671","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2015","journal-title":"IEEE Trans. Robot."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"ORB-SLAM2: An Open-source SLAM System for Monocular, Stereo, and RGB-D Cameras","volume":"33","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1874","DOI":"10.1109\/TRO.2021.3075644","article-title":"ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual\u2013Inertial, and Multimap SLAM","volume":"37","author":"Campos","year":"2021","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator","volume":"34","author":"Qin","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., and Cremers, D. (2014, January 6\u201312). LSD-SLAM: Large-Scale Direct Monocular SLAM. Proceedings of the Computer Vision ECCV 2014, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct Sparse Odometry","volume":"40","author":"Engel","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Klein, G., and Murray, D. (2007, January 13\u201316). Parallel Tracking and Mapping for Small AR Workspaces. Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, Nara, Japan.","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bakkay, M.C., Arafa, M., and Zagrouba, E. (2015, January 17\u201319). Dense 3D SLAM in Dynamic Scenes Using Kinect. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Compostela, Spain.","DOI":"10.1007\/978-3-319-19390-8_14"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2263","DOI":"10.1109\/LRA.2017.2724759","article-title":"RGB-D SLAM in Dynamic Environments Using Static Point Weighting","volume":"2","author":"Li","year":"2017","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, R., Wan, W., Wang, Y., and Di, K. (2019). A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes. Remote Sens., 11.","DOI":"10.3390\/rs11101143"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3703","DOI":"10.1109\/LRA.2021.3066375","article-title":"RigidFusion: Robot Localisation and Mapping in Environments with Large Dynamic Rigid Objects","volume":"6","author":"Long","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3821","DOI":"10.1007\/s13042-022-01627-2","article-title":"An Improved Adaptive ORB-SLAM Method for Monocular Vision Robot under Dynamic Environments","volume":"13","author":"Ni","year":"2022","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4681","DOI":"10.1109\/TIM.2019.2957849","article-title":"A Data-Flow Oriented Deep Ensemble Learning Method for Real-Time Surface Defect Inspection","volume":"69","author":"Liu","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Tunio, M.H., Li, J., Butt, M.H.F., Memon, I., and Magsi, Y. (2022, January 16\u201318). Fruit Detection and Segmentation Using Customized Deep Learning Techniques. Proceedings of the 2022 19th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), Chengdu, China.","DOI":"10.1109\/ICCWAMTIP56608.2022.10016600"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wang, K., Lin, Y., Wang, L., Han, L., Hua, M., Wang, X., Lian, S., and Huang, B. (2019, January 20\u201324). A Unified Framework for Mutual Improvement of SLAM and Semantic Segmentation. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793499"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Qi, H., Dai, J., Ji, X., and Wei, Y. (2017, January 21\u201326). Fully Convolutional Instance-Aware Semantic Segmentation. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.472"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Yuan, X., and Chen, S. (2020, January 25\u201329). SaD-SLAM: A Visual SLAM Based on Semantic and Depth Information. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341180"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"5191","DOI":"10.1109\/LRA.2021.3068640","article-title":"DynaSLAM II: Tightly-Coupled Multi-Object Tracking and SLAM","volume":"6","author":"Bescos","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"107822","DOI":"10.1016\/j.patcog.2021.107822","article-title":"Visual SLAM for Robot Navigation in Healthcare Facility","volume":"113","author":"Fang","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"4076","DOI":"10.1109\/LRA.2018.2860039","article-title":"DynaSLAM: Tracking, Mapping, and Inpainting in Dynamic Scenes","volume":"3","author":"Bescos","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"10818","DOI":"10.1109\/JSEN.2022.3169340","article-title":"WF-SLAM: A Robust VSLAM for Dynamic Scenarios via Weighted Features","volume":"22","author":"Zhong","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"6011","DOI":"10.1007\/s00521-021-06764-3","article-title":"YOLO-SLAM: A Semantic SLAM System towards Dynamic Environment with Geometric Constraint","volume":"34","author":"Wu","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_30","first-page":"1064","article-title":"ESD-SLAM: An Efficient Semantic Visual SLAM towards Dynamic Environments","volume":"42","author":"Xu","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1155\/2022\/7600669","article-title":"MISD-SLAM: Multimodal Semantic SLAM for Dynamic Environments","volume":"2022","author":"You","year":"2022","journal-title":"Wirel. Commun. Mob. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Yu, W., Liu, W., Xu, H., and He, Y. (2023). A Lightweight Visual Simultaneous Localization and Mapping Method with a High Precision in Dynamic Scenes. Sensors, 23.","DOI":"10.3390\/s23229274"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2023.3326234","article-title":"SG-SLAM: A Real-Time RGB-D Visual SLAM Toward Dynamic Scenes with Semantic and Geometric Information","volume":"72","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"125006","DOI":"10.1088\/1361-6501\/aceb7e","article-title":"SCE-SLAM: A Real-time Semantic RGBD SLAM System in Dynamic Scenes Based on Spatial Coordinate Error","volume":"34","author":"Song","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"19418","DOI":"10.1007\/s10489-023-04531-6","article-title":"Dynamic Visual Simultaneous Localization and Mapping Based on Semantic Segmentation Module","volume":"53","author":"Jin","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Sturm, J., Engelhard, N., Endres, F., Burgard, W., and Cremers, D. (2012, January 7\u201312). A Benchmark for the Evaluation of RGB-D SLAM Systems. Proceedings of the 2012 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Vilamoura, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yu, C., Liu, Z., Liu, X.J., Xie, F., Yang, Y., Wei, Q., and Fei, Q. (2018, January 1\u20135). DS-SLAM: A Semantic Visual SLAM towards Dynamic Environments. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593691"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"23772","DOI":"10.1109\/ACCESS.2021.3050617","article-title":"RDS-SLAM: Real-Time Dynamic SLAM Using Semantic Segmentation Methods","volume":"9","author":"Liu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"9573","DOI":"10.1109\/LRA.2022.3191193","article-title":"RGB-D Inertial Odometry for a Resource-Restricted Robot in Dynamic Environments","volume":"7","author":"Liu","year":"2022","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:18:29Z","timestamp":1760105909000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,25]]},"references-count":41,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072102"],"URL":"https:\/\/doi.org\/10.3390\/s24072102","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,25]]}}}