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Such challenges lead to subpar positioning accuracy and efficiency. This paper introduces a novel lightweight dynamic SLAM algorithm designed primarily to mitigate the interference caused by moving object occlusions. Our proposed approach combines a deep learning object detection algorithm with a Kalman filter. This combination offers prior information about dynamic objects for each SLAM algorithm frame. Leveraging geometric techniques like RANSAC and the epipolar constraint, our method filters out dynamic feature points, focuses on static feature points for pose determination, and enhances the SLAM algorithm\u2019s robustness in dynamic environments. We conducted experimental validations on the TUM public dataset, which demonstrated that our approach elevates positioning accuracy by approximately 54% and boosts the running speed by 75.47% in dynamic scenes.<\/jats:p>","DOI":"10.1017\/s0263574724000420","type":"journal-article","created":{"date-parts":[[2024,5,27]],"date-time":"2024-05-27T10:36:48Z","timestamp":1716806208000},"page":"2209-2225","source":"Crossref","is-referenced-by-count":5,"title":["Dynamic simultaneous localization and mapping based on object tracking in occluded environment"],"prefix":"10.1017","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1671-7789","authenticated-orcid":false,"given":"Weili","family":"Ding","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Pei","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taiyu","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"56","published-online":{"date-parts":[[2024,5,27]]},"reference":[{"key":"S0263574724000420_ref16","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2019.11.707"},{"key":"S0263574724000420_ref22","doi-asserted-by":"crossref","unstructured":"[22] Runz, M. , Buffier, M. and Agapito, L. , \u201cMaskfusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects,\u201d In:\u00a02018 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), (2018) pp. 10\u201320.","DOI":"10.1109\/ISMAR.2018.00024"},{"key":"S0263574724000420_ref15","doi-asserted-by":"crossref","unstructured":"[15] Demim, F. , Nemra, A. , Boucheloukh, A. , Louadj, K. and Bazoula, A. , \u201cRobust SVSF-SLAM Algorithm for Unmanned Vehicle in Dynamic Environment,\u201d In:\u00a02018 International Conference on Signal, Image, Vision and their Applications (SIVA), (2018) pp. 1\u20135.","DOI":"10.1109\/SIVA.2018.8660984"},{"key":"S0263574724000420_ref28","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2849609"},{"key":"S0263574724000420_ref7","doi-asserted-by":"publisher","DOI":"10.1145\/3177853"},{"key":"S0263574724000420_ref20","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574721001521"},{"key":"S0263574724000420_ref4","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-023-01812-7"},{"key":"S0263574724000420_ref25","doi-asserted-by":"crossref","unstructured":"[25] Rosten, E. and Drummond, T. , \u201cMachine Learning for High-Speed Corner Detection,\u201d In:\u00a0Computer Vision\u2013ECCV 2006: 9th European Conference on Computer Vision, (2006) pp. 430\u2013443.","DOI":"10.1007\/11744023_34"},{"key":"S0263574724000420_ref18","doi-asserted-by":"publisher","DOI":"10.1016\/j.ifacol.2016.10.585"},{"key":"S0263574724000420_ref23","unstructured":"[23] Zhang, J. , Henein, M. , Mahony, R. and Ila, V. , \u201cVdo-slam: A visual dynamic object-aware slam system, \u201c (2020). arXiv preprint arXiv: 2005.11052."},{"key":"S0263574724000420_ref8","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-022-01643-y"},{"key":"S0263574724000420_ref2","unstructured":"[2] Engelhard, N. , Endres, F. , Hess, J. , Sturm, J. and Burgard, W. , \u201cReal-Time 3D Visual SLAM with a Hand-Held RGB-D Camera,\u201d In:\u00a0Proc. of the RGB-D Workshop on 3D Perception in Robotics at the European Robotics Forum, (2011) pp. 1\u201315."},{"key":"S0263574724000420_ref5","doi-asserted-by":"crossref","unstructured":"[5] Forster, C. , Pizzoli, M. and Scaramuzza, D. , \u201cSvo: Fast Semi-Direct Monocular Visual Odometry,\u201d In:\u00a02014 IEEE international conference on robotics and automation (ICRA), (2014) pp. 15\u201322.","DOI":"10.1109\/ICRA.2014.6906584"},{"key":"S0263574724000420_ref19","doi-asserted-by":"publisher","DOI":"10.1109\/ICCSCE.2012.6487164"},{"key":"S0263574724000420_ref9","doi-asserted-by":"crossref","unstructured":"[9] Barath, D. , Cavalli, L. and Pollefeys, M. , \u201cLearning to Find Good Models in RANSAC,\u201d In:\u00a0Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, (2022) pp. 15744\u201315753.","DOI":"10.1109\/CVPR52688.2022.01529"},{"key":"S0263574724000420_ref21","doi-asserted-by":"crossref","unstructured":"[21] Scona, R. , Jaimez, M. , Petillot, Y. 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