{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:49:06Z","timestamp":1782103746021,"version":"3.54.5"},"reference-count":18,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T00:00:00Z","timestamp":1781222400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3900804"],"award-info":[{"award-number":["2021YFB3900804"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In complex urban environments, Global Navigation Satellite System (GNSS) signals are easily affected by building blockage and multipath effects, which may degrade positioning quality or even cause GNSS denial. As a result, conventional integrated navigation systems suffer from accumulated errors due to insufficient global constraints. To address this problem, a multi-source integrated positioning method incorporating semantic information is proposed. Fixed traffic lights are selected as semantic landmarks, and an object detection network is used to extract the center pixel coordinates and detection confidence of the landmarks. Then, by combining depth information, camera pose, and the prior global coordinates of fixed semantic landmarks, a semantic target inversion model is established to transform two-dimensional image information into three-dimensional position estimates in the world coordinate system. Semantic factors are further constructed and incorporated into backend factor graph optimization. To determine the weighting of semantic factors, the influences of pixel localization error, depth estimation error, camera pose error, and prior coordinate error of fixed semantic landmarks on semantic observations are analyzed, and a noise covariance model for semantic factors is established. Finally, an unmanned ground vehicle experimental platform is built to validate and analyze the proposed factor graph algorithm. The experimental results show that, under GNSS-degraded conditions, the algorithm with semantic factors can provide supplementary global constraints for the system and effectively suppress accumulated positioning errors. In Experiment 1, compared with the algorithm without semantic factors, the maximum absolute trajectory error is reduced by 46.26%. To further verify the applicability of the proposed method in more complex scenarios, Experiment 2 is conducted on a longer route with multiple semantic landmarks and a more severe GNSS-degraded interval. The results show that the proposed method reduces the maximum APE from 6.5432 m to 3.4778 m, corresponding to a reduction of approximately 46.85%. These results demonstrate that the proposed semantic factor can improve the robustness of multi-source fusion localization in GNSS-degraded environments.<\/jats:p>","DOI":"10.3390\/s26123761","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:59:47Z","timestamp":1781503187000},"page":"3761","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Semantic-Enhanced Multi-Source Fusion Localization Method for GNSS-Degraded Environments"],"prefix":"10.3390","volume":"26","author":[{"given":"Haobo","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinhua","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,12]]},"reference":[{"key":"ref_1","first-page":"274","article-title":"A review of low-altitude vision datasets for UAVs","volume":"40","author":"Sun","year":"2025","journal-title":"J. Data Acquis. Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014). LOAM: Lidar odometry and mapping in real-time. Robotics: Science and Systems, MIT Press.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018). Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3317","DOI":"10.1109\/LRA.2021.3064227","article-title":"Fast-lio: A fast, robust lidar-inertial odometry package by tightly-coupled iterated kalman filter","volume":"6","author":"Xu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2053","DOI":"10.1109\/TRO.2022.3141876","article-title":"Fast-lio2: Fast direct lidar-inertial odometry","volume":"38","author":"Xu","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1177\/0278364911430419","article-title":"iSAM2: Incremental smoothing and mapping using the Bayes tree","volume":"31","author":"Kaess","year":"2012","journal-title":"Int. J. Robot. Res."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., and Rus, D. (2020). Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping. Proceedings of the 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yu, C., Liu, Z., Liu, X.J., Xie, F., Yang, Y., Wei, Q., and Fei, Q. (2018). DS-SLAM: A semantic visual SLAM towards dynamic environments. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8593691"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TRO.2019.2909168","article-title":"CubeSLAM: Monocular 3-D object SLAM","volume":"35","author":"Yang","year":"2019","journal-title":"IEEE Trans. Robot."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/LRA.2018.2866205","article-title":"QuadricSLAM: Dual quadrics from object detections as landmarks in object-oriented SLAM","volume":"4","author":"Nicholson","year":"2018","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_12","unstructured":"Pfrommer, B., and Daniilidis, K. (2019, January 20\u201324). TagSLAM: Robust SLAM with fiducial markers. Proceedings of the 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Qin, T., Chen, T., Chen, Y., Cai, Z., and Wang, X. (June, January 30). RoadMap: A light-weight semantic map for visual localization towards autonomous driving. Proceedings of the 2021 IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China.","DOI":"10.1109\/ICRA48506.2021.9561663"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Barrau, A., and Bonnabel, S. (2020). A mathematical framework for IMU error propagation with applications to preintegration. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA40945.2020.9197492"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. Proceedings of the 2011 International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_16","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_17","doi-asserted-by":"crossref","unstructured":"Zhang, F. (2006). The Schur Complement and Its Applications, Springer Science & Business Media.","DOI":"10.1007\/b105056"},{"key":"ref_18","unstructured":"Ultralytics (2026, May 13). YOLOv8: A New State-of-the-Art Computer Vision Model. Available online: https:\/\/docs.ultralytics.com\/models\/yolov8\/."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3761\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,22]],"date-time":"2026-06-22T04:20:21Z","timestamp":1782102021000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3761"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,12]]},"references-count":18,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123761"],"URL":"https:\/\/doi.org\/10.3390\/s26123761","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,12]]}}}