{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T00:30:57Z","timestamp":1759969857030,"version":"build-2065373602"},"reference-count":43,"publisher":"Cambridge University Press (CUP)","issue":"9","license":[{"start":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T00:00:00Z","timestamp":1758067200000},"content-version":"unspecified","delay-in-days":16,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Robotica"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Visual Simultaneous Localization and Mapping (vSLAM) is essentially limited by the static world assumption, which makes its application in dynamic environments challenging. This paper proposes a robust vSLAM system, RFN-SLAM, which is based on ORB-SLAM3 and does not require preset dynamic labels and weighted features to process dynamic scenes. In the feature extraction stage, an enhanced efficient binary image BAD descriptor is used to improve the accuracy of static feature point matching. Through the improved RT-DETR target detection network and FAST-SAM instance segmentation network, RFN-SLAM obtains semantic information and uses a novel dynamic box detection algorithm to identify and eliminate the feature points of dynamic objects. When optimizing the pose, the static feature points are weighted according to the dynamic information, which significantly reduces the mismatch and improves the accuracy of positioning. Meanwhile, 3D rendering of the neural radiation field is used to remove dynamic objects and render them. Experiments were conducted on the TUM RGB-D dataset, Bonn dataset, and self-collected dataset. The results show that in terms of positioning accuracy, RFN-SLAM significantly outperforms ORB-SLAM3 in dynamic environments. It also achieves more accurate positioning than other advanced dynamic SLAM methods and successfully realizes accurate 3D reconstruction of static scenes. In addition, on the premise of ensuring accuracy, the real-time performance of RFN-SLAM is effectively guaranteed.<\/jats:p>","DOI":"10.1017\/s0263574725102506","type":"journal-article","created":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T07:35:13Z","timestamp":1758094513000},"page":"3366-3394","source":"Crossref","is-referenced-by-count":0,"title":["A robust visual simultaneous localization and mapping system for dynamic environments without predefined dynamic labels and weighted features"],"prefix":"10.1017","volume":"43","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7268-5094","authenticated-orcid":false,"given":"Shuai","family":"Xiang","sequence":"first","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyi","family":"Dong","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"},{"name":"Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1942-0464","authenticated-orcid":false,"given":"Kang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ge","family":"Tai","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tianyu","family":"Yuan","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haoda","family":"Yan","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyan","family":"Chen","sequence":"additional","affiliation":[{"name":"Inner Mongolia University of Technology"},{"name":"Universities in Inner Mongolia Autonomous Region"},{"name":"Ministry of Education"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"56","published-online":{"date-parts":[[2025,9,17]]},"reference":[{"key":"S0263574725102506_ref27","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574724001553"},{"key":"S0263574725102506_ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2015.2463671"},{"key":"S0263574725102506_ref37","unstructured":"[37] Zhang, J. , Henein, M. , Mahony, R. and Ila, V. , \u201cVDO-SLAM: A visual dynamic object-aware SLAM system.\u201d arxiv preprint arxiv:2005.11052 (2020)."},{"key":"S0263574725102506_ref1","doi-asserted-by":"crossref","first-page":"2496","DOI":"10.3390\/rs15102496","article-title":"An overview of key SLAM technologies for underwater scenes","volume":"15","author":"Wang","year":"2023","journal-title":"Remote Sens-BASEL"},{"key":"S0263574725102506_ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.robot.2016.11.012"},{"key":"S0263574725102506_ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2021.3075644"},{"key":"S0263574725102506_ref12","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2018.2860039"},{"key":"S0263574725102506_ref22","doi-asserted-by":"publisher","DOI":"10.3390\/rs11101143"},{"key":"S0263574725102506_ref2","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. Rob. Syst."},{"key":"S0263574725102506_ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ISMAR.2007.4538852"},{"key":"S0263574725102506_ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2017.2705103"},{"key":"S0263574725102506_ref28","doi-asserted-by":"crossref","first-page":"2367","DOI":"10.1017\/S0263574724000754","article-title":"CPR-SLAM: RGB-D SLAM in dynamic environment using sub-point cloud correlations","volume":"42","author":"Yu","year":"2024","journal-title":"Robotica"},{"key":"S0263574725102506_ref17","doi-asserted-by":"publisher","DOI":"10.1145\/3503250"},{"key":"S0263574725102506_ref9","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1007\/s11370-023-00478-2","article-title":"IQ-VIO: Adaptive visual inertial odometry via interference quantization under dynamic environments","volume":"16","author":"Zhang","year":"2023","journal-title":"Intell. Serv. Rob."},{"key":"S0263574725102506_ref13","doi-asserted-by":"crossref","unstructured":"[13] Yu, C. , Liu, Z. , Liu, X. J. , Xie, F. , Yang, Y. , Wei, Q. and Fei, Q. , \u201cDS-SLAM: A Semantic Visual SLAM Towards Dynamic Environments,\u201d 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), 1 Oct 2018 (IEEE, 2018) pp. 1168\u20131174.","DOI":"10.1109\/IROS.2018.8593691"},{"key":"S0263574725102506_ref20","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2021.3107024"},{"key":"S0263574725102506_ref16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3528223.3530127","article-title":"Instant neural graphics primitives with a multiresolution hash encoding","volume":"41","author":"M\u00fcller","year":"2022","journal-title":"ACM Trans. Graphics (TOG)"},{"key":"S0263574725102506_ref14","first-page":"1","article-title":"Optimization RGB-D 3-D reconstruction algorithm based on dynamic SLAM","volume":"23","author":"Pan","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"S0263574725102506_ref18","doi-asserted-by":"crossref","unstructured":"[18] Zhao, Y. , Lv, W. , Xu, S. , Wei, J. , Wang, G. , Dang, Q. , Liu, Y. and Chen, J. , \u201cDetrs Beat Yolos on Real-Time Object Detection,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2024) pp. 16965\u201316974.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"S0263574725102506_ref32","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2023.3270534"},{"key":"S0263574725102506_ref21","doi-asserted-by":"publisher","DOI":"10.1109\/TRO.2016.2609395"},{"key":"S0263574725102506_ref35","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2017.2724759"},{"key":"S0263574725102506_ref23","first-page":"1","article-title":"DGM-VINS: Visual\u2013inertial 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":"S0263574725102506_ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-021-01414-1"},{"key":"S0263574725102506_ref15","doi-asserted-by":"publisher","DOI":"10.1109\/TIM.2023.3326234"},{"key":"S0263574725102506_ref24","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3010942"},{"key":"S0263574725102506_ref19","unstructured":"[19] Zhao, X. , Ding, W. , An, Y. , Du, Y. , Yu, T. , Li, M. , Tang, M. and Wang, J. , \u201cFast segment anything. arxiv preprint arxiv:2306.12156 (2023)."},{"key":"S0263574725102506_ref34","doi-asserted-by":"crossref","first-page":"085202","DOI":"10.1088\/1361-6501\/acd1a4","article-title":"Semantic SLAM for mobile robots in dynamic environments based on visual camera sensors","volume":"34","author":"Zhang","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"S0263574725102506_ref10","first-page":"5538840","article-title":"OFM-SLAM: A visual semantic SLAM for dynamic indoor environments","volume":"2021","author":"Zhao","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"S0263574725102506_ref38","doi-asserted-by":"crossref","unstructured":"[38] Yao, Y. , Luo, Z. , Li, S. , Fang, T. and Quan, L. , \u201cMvsnet: Depth Inference for Unstructured Multi-view Stereo,\u201d Proceedings of the European Conference on Computer Vision (ECCV) (2018) pp. 767\u2013783.","DOI":"10.1007\/978-3-030-01237-3_47"},{"key":"S0263574725102506_ref39","doi-asserted-by":"crossref","unstructured":"[39] Yao, Y. , Luo, Z. , Li, S. , Shen, T. , Fang, T. and Quan, L. , \u201cRecurrent MVSNet for High-Resolution Multi-view Stereo Depth Inference,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2019) pp. 5525\u20135534.","DOI":"10.1109\/CVPR.2019.00567"},{"key":"S0263574725102506_ref40","doi-asserted-by":"publisher","DOI":"10.1145\/3592433"},{"key":"S0263574725102506_ref41","doi-asserted-by":"crossref","unstructured":"[41] Liu, X. , Peng, H. , Zheng, N. , Yang, Y. , Hu, H. and Yuan, Y. , \u201cEfficientvit: Memory Efficient Vision Transformer with Cascaded Group Attention,\u201d Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2023) pp. 14420\u201314430.","DOI":"10.1109\/CVPR52729.2023.01386"},{"key":"S0263574725102506_ref4","doi-asserted-by":"crossref","unstructured":"[4] Engel, J. , Sch\u00f6ps, T. and Cremers, D. , \u201cLSD-SLAM: Large-Scale Direct Monocular SLAM,\u201d European Conference on Computer Vision, 6 Sep 2014 (Springer International Publishing, Cham, 2014) pp. 834\u2013849.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"S0263574725102506_ref43","doi-asserted-by":"publisher","DOI":"10.1109\/IROS40897.2019.8967590"},{"key":"S0263574725102506_ref30","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574724000511"},{"key":"S0263574725102506_ref33","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2020.3028218"},{"key":"S0263574725102506_ref11","doi-asserted-by":"crossref","first-page":"6846","DOI":"10.1109\/LRA.2022.3178150","article-title":"Twistslam: Constrained slam in dynamic environment","volume":"7","author":"Gonzalez","year":"2022","journal-title":"IEEE Rob. Autom. Lett."},{"key":"S0263574725102506_ref26","doi-asserted-by":"crossref","unstructured":"[26] Ji, T. , Wang, C. and Xie, L. , \u201cTowards Real-Time Semantic RGB-D SLAM in Dynamic Environments,\u201d 2021 IEEE International Conference on Robotics and Automation (ICRA), 30 May 2021 (IEEE) pp. 11175\u201311181.","DOI":"10.1109\/ICRA48506.2021.9561743"},{"key":"S0263574725102506_ref29","doi-asserted-by":"publisher","DOI":"10.1017\/S0263574721001521"},{"key":"S0263574725102506_ref31","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108225"},{"key":"S0263574725102506_ref36","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2022.3169340"},{"key":"S0263574725102506_ref42","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2012.6385773"}],"container-title":["Robotica"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S0263574725102506","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T09:03:23Z","timestamp":1759914203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S0263574725102506\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9]]},"references-count":43,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["S0263574725102506"],"URL":"https:\/\/doi.org\/10.1017\/s0263574725102506","relation":{},"ISSN":["0263-5747","1469-8668"],"issn-type":[{"type":"print","value":"0263-5747"},{"type":"electronic","value":"1469-8668"}],"subject":[],"published":{"date-parts":[[2025,9]]}}}