{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T04:59:45Z","timestamp":1779166785941,"version":"3.51.4"},"reference-count":23,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:00:00Z","timestamp":1767052800000},"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":"crossref","award":["62273123"],"award-info":[{"award-number":["62273123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To obtain accurate location information in dynamic environments, we propose a dynamic visual\u2013inertial SLAM algorithm that can operate in real-time. In this paper, we combine the YOLO-V5 algorithm and the depth threshold extraction algorithm to achieve real-time pixel-level segmentation of objects. Meanwhile, to address the situation where dynamic targets are occluded by other objects, we design the object depth extraction method based on K-means clustering. We also design a factor graph optimization with rigid and non-rigid dynamic objects based on object category division, in order to better utilize the motion information of dynamic objects. We use the Kalman filter algorithm to achieve object matching and tracking. At the same time, to obtain as many rigid targets as possible, we design the adaptive rigid point set modeling algorithm to further supplement the rigid objects. Finally, we evaluate the algorithm through public datasets and self-built datasets, verifying its ability to handle dynamic environments.<\/jats:p>","DOI":"10.3390\/s26010235","type":"journal-article","created":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T14:33:25Z","timestamp":1767191605000},"page":"235","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Dynamic SLAM by Combining Rigid Feature Point Set Modeling and YOLO"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9934-5778","authenticated-orcid":false,"given":"Pengchao","family":"Ding","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1236-8350","authenticated-orcid":false,"given":"Weidong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xian","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kangle","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Wu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijiang","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin University of Technology, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mourikis, A.I., and Roumeliotis, S.I. (2007, January 10\u201314). A multi-state constraint kalman filter for vision-aided inertial navigation. Proceedings of the 2007 IEEE International Conference on Robotics and Automation, Rome, Italy.","DOI":"10.1109\/ROBOT.2007.364024"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1177\/0278364913481251","article-title":"High-precision, consistent ekf-based visual-inertial odometry","volume":"32","author":"Li","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1109\/TRO.2018.2853729","article-title":"Vins-mono: A robust and versati-lemonocular visual-inertial state estimator","volume":"34","author":"Qin","year":"2018","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e70023","DOI":"10.1049\/csy2.70023","article-title":"A general optimization-based framework for global pose estimation with multiple sensors","volume":"7","author":"Qin","year":"2025","journal-title":"IET Cyber-Syst. Robot."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TRO.2017.2705103","article-title":"Orb-slam2: An open-sourceslam system for monocular, stereo, and rgb-d cameras","volume":"33","author":"Tard","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Lu, X., Wang, H., Tang, S., Huang, H., and Li, C. (2020). Dm-slam: Monocular slam in dynamic environments. Appl. Sci., 10.","DOI":"10.20944\/preprints202001.0123.v1"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll, 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_14","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_15","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_16","doi-asserted-by":"crossref","unstructured":"Cong, P., Liu, J., Li, J., Xiao, Y., Chen, X., Feng, X., and Zhang, X. (2023). YDD-SLAM: Indoor dynamic visual slam fusing yolov5 with depth information. Sensors, 23.","DOI":"10.3390\/s23239592"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Kan, X., Shi, G., Yang, X., and Hu, X. (2024). YPR-SLAM: A SLAM system combining object detection and geometric constraints for dynamic scenes. Sensors, 24.","DOI":"10.3390\/s24206576"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wei, W., Xia, C., and Han, J. (2025). DI-SLAM: A Real-Time Enhanced RGB-D SLAM for Dynamic Indoor Environments. Appl. Sci., 15.","DOI":"10.3390\/app15084446"},{"key":"ref_19","unstructured":"Zhang, J., Henein, M., Mahony, R., and Ila, V. (2020). VDO-SLAM: A visual dynamic object-aware slam system. arXiv."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Wang, C., Wang, W., Henein, M., and Scherer, S. (2022, January 23\u201327). AirDOS: Dynamic slam benefits from articulated objects. Proceedings of the 2022 International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811667"},{"key":"ref_22","unstructured":"MacQueen, J. (July, January 21). Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, USA."},{"key":"ref_23","unstructured":"Grupp, M. (2025, March 02). evo: Python Package for the Evaluation of o-Dometry and SLAM. Available online: https:\/\/github.com\/MichaelGrupp\/evo."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/235\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T05:30:58Z","timestamp":1767245458000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/235"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,30]]},"references-count":23,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26010235"],"URL":"https:\/\/doi.org\/10.3390\/s26010235","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,30]]}}}