{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T04:07:24Z","timestamp":1775102844255,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:00:00Z","timestamp":1721347200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51774235"],"award-info":[{"award-number":["51774235"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021GY-338"],"award-info":[{"award-number":["2021GY-338"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["GX2333"],"award-info":[{"award-number":["GX2333"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Provincial Key R&amp;D General Industrial Project","award":["51774235"],"award-info":[{"award-number":["51774235"]}]},{"name":"Shaanxi Provincial Key R&amp;D General Industrial Project","award":["2021GY-338"],"award-info":[{"award-number":["2021GY-338"]}]},{"name":"Shaanxi Provincial Key R&amp;D General Industrial Project","award":["GX2333"],"award-info":[{"award-number":["GX2333"]}]},{"name":"Xi\u2019an Beilin District Science and Technology Plan Project","award":["51774235"],"award-info":[{"award-number":["51774235"]}]},{"name":"Xi\u2019an Beilin District Science and Technology Plan Project","award":["2021GY-338"],"award-info":[{"award-number":["2021GY-338"]}]},{"name":"Xi\u2019an Beilin District Science and Technology Plan Project","award":["GX2333"],"award-info":[{"award-number":["GX2333"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>SLAM is a critical technology for enabling autonomous navigation and positioning in unmanned vehicles. Traditional visual simultaneous localization and mapping algorithms are built upon the assumption of a static scene, overlooking the impact of dynamic targets within real-world environments. Interference from dynamic targets can significantly degrade the system\u2019s localization accuracy or even lead to tracking failure. To address these issues, we propose a dynamic visual SLAM system named BY-SLAM, which is based on BEBLID and semantic information extraction. Initially, the BEBLID descriptor is introduced to describe Oriented FAST feature points, enhancing both feature point matching accuracy and speed. Subsequently, FasterNet replaces the backbone network of YOLOv8s to expedite semantic information extraction. By using the results of DBSCAN clustering object detection, a more refined semantic mask is obtained. Finally, by leveraging the semantic mask and epipolar constraints, dynamic feature points are discerned and eliminated, allowing for the utilization of only static feature points for pose estimation and the construction of a dense 3D map that excludes dynamic targets. Experimental evaluations are conducted on both the TUM RGB-D dataset and real-world scenarios and demonstrate the effectiveness of the proposed algorithm at filtering out dynamic targets within the scenes. On average, the localization accuracy for the TUM RGB-D dataset improves by 95.53% compared to ORB-SLAM3. Comparative analyses against classical dynamic SLAM systems further corroborate the improvement in localization accuracy, map readability, and robustness achieved by BY-SLAM.<\/jats:p>","DOI":"10.3390\/s24144693","type":"journal-article","created":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T14:16:38Z","timestamp":1721398598000},"page":"4693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["BY-SLAM: Dynamic Visual SLAM System Based on BEBLID and Semantic Information Extraction"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4757-4194","authenticated-orcid":false,"given":"Daixian","family":"Zhu","sequence":"first","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peixuan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Qiu","sequence":"additional","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaxin","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruolin","family":"Gong","sequence":"additional","affiliation":[{"name":"College of Communication and Information Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wu, Y., Tong, K., Chen, H., and Yuan, Y. (2023). Review of Visual Simultaneous Localization and Mapping Based on Deep Learning. Remote Sens., 15.","DOI":"10.3390\/rs15112740"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sandstr\u00f6m, E., Li, Y., Van Gool, L., and Oswald, M.R. (2023, January 1\u20136). Point-slam: Dense neural point cloud-based slam. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01690"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vidanapathirana, K., Moghadam, P., Harwood, B., Zhao, M., Sridharan, S., and Fookes, C. (June, January 30). Locus: Lidar-based place recognition using spatiotemporal higher-order pooling. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9560915"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1109\/TPAMI.2007.1049","article-title":"MonoSLAM: Real-time single camera SLAM","volume":"29","author":"Davison","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_5","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_6","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 European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_7","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_8","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_9","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_10","doi-asserted-by":"crossref","first-page":"22119","DOI":"10.1109\/JSEN.2023.3306371","article-title":"Visual SLAM integration with semantic segmentation and deep learning: A review","volume":"23","author":"Pu","year":"2023","journal-title":"IEEE Sens. J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_12","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_13","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/TPAMI.2020.3010942","article-title":"Rgb-d slam in dynamic environments using point correlations","volume":"44","author":"Dai","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lu, Q., Pan, Y., Hu, L., and He, J. (2023). A Method for Reconstructing Background from RGB-D SLAM in Indoor Dynamic Environments. Sensors, 23.","DOI":"10.3390\/s23073529"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"8503711","DOI":"10.1109\/TIM.2023.3280533","article-title":"DGM-VINS: Visual-Inertial SLAM for Complex Dynamic Environments with Joint Geometry Feature Extraction and Multiple Object Tracking","volume":"72","author":"Song","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Zhong, F., Wang, S., Zhang, Z., and Wang, Y. (2018, January 12\u201315). Detect-SLAM: Making object detection and SLAM mutually beneficial. Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Tahoe, NV, USA.","DOI":"10.1109\/WACV.2018.00115"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Henein, M., Zhang, J., Mahony, R., and Ila, V. (August, January 31). Dynamic SLAM: The need for speed. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9196895"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"10878","DOI":"10.1109\/JSEN.2024.3365822","article-title":"Real-time dynamic SLAM using moving probability based on IMU and segmentation","volume":"24","author":"Zhang","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_20","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_21","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_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.robot.2019.03.012","article-title":"Dynamic-SLAM: Semantic monocular visual localization and mapping based on deep learning in dynamic environment","volume":"117","author":"Xiao","year":"2019","journal-title":"Robot. Auton. Syst."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","first-page":"108225","DOI":"10.1016\/j.patcog.2021.108225","article-title":"Blitz-SLAM: A semantic SLAM in dynamic environments","volume":"121","author":"Fan","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_25","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."},{"key":"ref_26","unstructured":"Ye, W., Yu, X., Lan, X., Ming, Y., Li, J., Bao, H., Cui, Z., and Zhang, G. (2022). Deflowslam: Self-supervised scene motion decomposition for dynamic dense slam. arXiv."},{"key":"ref_27","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_28","doi-asserted-by":"crossref","first-page":"14596","DOI":"10.1109\/JSEN.2024.3373892","article-title":"DPL-SLAM: Enhancing Dynamic Point-Line SLAM through Dense Semantic Methods","volume":"24","author":"Lin","year":"2024","journal-title":"IEEE Sens. J."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.patrec.2020.04.005","article-title":"BEBLID: Boosted efficient binary local image descriptor","volume":"133","author":"Sfeir","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.H., He, H., Zhuo, W., Wen, S., Lee, C.H., and Chan, S.H.G. (2023, January 17\u201324). Run, Don\u2019t walk: Chasing higher FLOPS for faster neural networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1145\/235815.235821","article-title":"The quickhull algorithm for convex hulls","volume":"22","author":"Barber","year":"1996","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Triggs, B., McLauchlan, P.F., Hartley, R.I., and Fitzgibbon, A.W. (1999, January 21\u201322). Bundle adjustment\u2014A modern synthesis. Proceedings of the Vision Algorithms: Theory and Practice: International Workshop on Vision Algorithms Corfu, Greece.","DOI":"10.1007\/3-540-44480-7_21"},{"key":"ref_36","unstructured":"Bolya, D., Zhou, C., Xiao, F., and Lee, Y.J. (November, January 27). Yolact: Real-time instance segmentation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_37","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., and Xu, C. (2020, January 13\u201319). Ghostnet: More features from cheap operations. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Su\u00e1rez, I., Sfeir, G., Buenaposada, J.M., and Baumela, L. (2019, January 1\u20134). BELID: Boosted efficient local image descriptor. Proceedings of the Iberian Conference on Pattern Recognition and Image Analysis, Madrid, Spain.","DOI":"10.1007\/978-3-030-31332-6_39"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1109\/TPAMI.2010.54","article-title":"Discriminative learning of local image descriptors","volume":"33","author":"Brown","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_42","unstructured":"Mehta, S., and Rastegari, M. (2021). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 11\u201317). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_45","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, Vilamoura-Algarve, Portugal.","DOI":"10.1109\/IROS.2012.6385773"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Balntas, V., Lenc, K., Vedaldi, A., and Mikolajczyk, K. (2017, January 21\u201326). HPatches: A benchmark and evaluation of handcrafted and learned local descriptors. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.410"},{"key":"ref_47","unstructured":"Grupp, M. (2024, May 24). Evo: Python Package for the Evaluation of Odometry and Slam. Available online: https:\/\/github.com\/MichaelGrupp\/evo."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4693\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:19:53Z","timestamp":1760109593000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/14\/4693"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,19]]},"references-count":47,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["s24144693"],"URL":"https:\/\/doi.org\/10.3390\/s24144693","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7,19]]}}}