{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T05:16:36Z","timestamp":1766553396331,"version":"3.48.0"},"reference-count":24,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"crossref","award":["ZR2017MF036"],"award-info":[{"award-number":["ZR2017MF036"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Defense Science and Technology Project Fund","award":["F062102009"],"award-info":[{"award-number":["F062102009"]}]},{"name":"Youth Innovation Team Program of Shandong Provincial Colleges and Universities","award":["2020KJN003"],"award-info":[{"award-number":["2020KJN003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To address the problem of degraded positioning accuracy in traditional visual\u2013inertial navigation systems (VINS) due to interference from moving objects in dynamic scenarios, this paper proposes an improved algorithm based on the VINS-Fusion framework, which resolves this issue through a synergistic combination of multi-scale feature optimization and real-time dynamic feature elimination. First, at the feature extraction front-end, the SuperPoint encoder structure is reconstructed. By integrating dual-branch multi-scale feature fusion and 1 \u00d7 1 convolutional channel compression, it simultaneously captures shallow texture details and deep semantic information, enhances the discriminative ability of static background features, and reduces mis-elimination near dynamic\u2013static boundaries. Second, in the dynamic processing module, the ASORT (Adaptive Simple Online and Realtime Tracking) algorithm is designed. This algorithm combines an object detection network, adaptive Kalman filter-based trajectory prediction, and a Hungarian algorithm-based matching mechanism to identify moving objects in images in real time, filter out their associated dynamic feature points from the optimized feature point set, and ensure that only reliable static features are input to the backend optimization, thereby minimizing pose estimation errors caused by dynamic interference. Experiments on the KITTI dataset demonstrate that, compared with the original VINS-Fusion algorithm, the proposed method achieves an average improvement of approximately 14.8% in absolute trajectory accuracy, with an average single-frame processing time of 23.9 milliseconds. This validates that the proposed approach provides an efficient and robust solution for visual\u2013inertial navigation in highly dynamic environments.<\/jats:p>","DOI":"10.3390\/s26010052","type":"journal-article","created":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T14:27:47Z","timestamp":1766500067000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Feature Elimination-Based Visual\u2013Inertial Navigation Algorithm"],"prefix":"10.3390","volume":"26","author":[{"given":"Jiawei","family":"Yu","sequence":"first","affiliation":[{"name":"Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongde","family":"Dai","sequence":"additional","affiliation":[{"name":"Naval Aviation University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mathematics and Statistical Sciences, Ludong University, Yantai 264001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Li","sequence":"additional","affiliation":[{"name":"Unit 92728 of the People\u2019s Liberation Army, Shanghai 200436, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xueying","family":"Liu","sequence":"additional","affiliation":[{"name":"Unit 92742 of the People\u2019s Liberation Army, Qinhuangdao 066000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1686","DOI":"10.1109\/TRO.2023.3248510","article-title":"A survey on active simultaneous localization and mapping: State of the art and new frontiers","volume":"39","author":"Placed","year":"2023","journal-title":"IEEE Trans. Robot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1186\/s41074-017-0027-2","article-title":"Visual SLAM algorithms: A survey from 2010 to 2016","volume":"9","author":"Taketomi","year":"2017","journal-title":"IPSJ Trans. Comput. Vis. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yuan, H., Han, K., and Lou, B. (2024). Mix-VIO: A visual inertial odometry based on a hybrid tracking strategy. Sensors, 24.","DOI":"10.3390\/s24165218"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"117734","DOI":"10.1016\/j.eswa.2022.117734","article-title":"A survey of state-of-the-art on visual SLAM","volume":"205","author":"Kazerouni","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kochman, M.T., Kielar, A., Kasprzak, M., Kasperek, W., Dutko, M., Vellender, A., Przysada, G., and Dru\u017cbicki, M. (2025). A Reliability Study of Small, Portable, Easy-to-Use, and IMU-Based Sensors for Gait Assessment. Sensors, 25.","DOI":"10.3390\/s25216597"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chen, W., Shang, G., Ji, 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_7","doi-asserted-by":"crossref","unstructured":"Ma, F., Shi, J., Yang, Y., Li, J., and Dai, K. (2019). ACK-MSCKF: Tightly-coupled Ackermann multi-state constraint Kalman filter for autonomous vehicle localization. Sensors, 19.","DOI":"10.3390\/s19214816"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bloesch, M., Omari, S., Hutter, M., and Siegwart, R. (October, January 28). Robust visual inertial odometry using a direct EKF-based approach. Proceedings of the 2015 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany.","DOI":"10.1109\/IROS.2015.7353389"},{"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":"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_11","doi-asserted-by":"crossref","unstructured":"Xu, B., and Dai, Q. (2023, January 13\u201315). Enhanced VINS-fusion-based SLAM algorithm for reliable camera pose estimation in GNSS-challenging environments. Proceedings of the 2023 IEEE International Conference on Unmanned Systems (ICUS), Hefei, China.","DOI":"10.1109\/ICUS58632.2023.10318438"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Su, Y., Wang, T., Yao, C., Shao, S., and Wang, Z. (2020\u201324, January 24). GR-SLAM: Vision-based sensor fusion SLAM for ground robots on complex terrain. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA.","DOI":"10.1109\/IROS45743.2020.9341387"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Wang, S., Hu, Q., Zhang, X., Li, W., Wang, Y., and Zheng, E. (2025). LVID-SLAM: A Lightweight Visual-Inertial SLAM for Dynamic Scenes Based on Semantic Information. Sensors, 25.","DOI":"10.3390\/s25134117"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Mao, H., and Luo, J. (2025). PLY-SLAM: Semantic Visual SLAM Integrating Point\u2013Line Features with YOLOv8-seg in Dynamic Scenes. Sensors, 25.","DOI":"10.3390\/s25123597"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nam, D.V., and Gon-Woo, K. (2020). Robust stereo visual inertial navigation system based on multi-stage outlier removal in dynamic environments. Sensors, 20.","DOI":"10.3390\/s20102922"},{"key":"ref_16","first-page":"323","article-title":"A comparison of SIFT and SURF","volume":"1","author":"Panchal","year":"2013","journal-title":"Int. J. Innov. Res. Comput. Commun. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"176","DOI":"10.5201\/ipol.2015.69","article-title":"An analysis of the SURF method","volume":"5","author":"Oyallon","year":"2015","journal-title":"Image Process. Line"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Juli\u00e0, L.F., and Monasse, P. (2017). A critical review of the trifocal tensor estimation. Pacific-Rim Symposium on Image and Video Technology, Springer.","DOI":"10.1007\/978-3-319-75786-5_28"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","unstructured":"DeTone, D., Malisiewicz, T., and Rabinovich, A. (2018, January 18\u201322). Superpoint: Self-supervised interest point detection and description. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00060"},{"key":"ref_22","first-page":"1","article-title":"Real-time video stabilization algorithm based on superpoint","volume":"73","author":"Liu","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bewley, A., Ge, Z., Ott, L., Ramos, F., and Upcroft, B. (2016, January 25\u201328). Simple online and realtime tracking. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533003"},{"key":"ref_24","unstructured":"Nasseri, M.H., Moradi, H., Hosseini, R., and Babaee, M. (2021). Simple online and real-time tracking with occlusion handling. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/52\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T05:13:38Z","timestamp":1766553218000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/1\/52"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,20]]},"references-count":24,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,1]]}},"alternative-id":["s26010052"],"URL":"https:\/\/doi.org\/10.3390\/s26010052","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,12,20]]}}}