{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T04:58:34Z","timestamp":1785560314458,"version":"3.56.0"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Youth Foundation Project","award":["42105143"],"award-info":[{"award-number":["42105143"]}]},{"name":"The Science and Technology Development Fund of Wuxi","award":["N20201011"],"award-info":[{"award-number":["N20201011"]}]},{"award":["N20201011"],"award-info":[{"award-number":["N20201011"]}],"id":[{"id":"https:\/\/ror.org\/02hkzh190","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Vehicle road collaboration application scenario validation","award":["560122034"],"award-info":[{"award-number":["560122034"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Aiming at the current SLAM (Simultaneous Localization and Mapping) algorithms in urban scenarios, which have problems such as elevation drift, odometry drift, and the appearance of false loop closures, a tightly coupled SLAM method with LiDAR and inertial guidance is proposed. In the front-end, a raster-based point cloud feature extraction method is introduced, enabling simultaneous segmentation and extraction of line, surface, and ground features. Utilizing the alignment results of line and surface features as the initial value for ground point alignment, interpolation weights are determined based on roll and pitch angle errors, effectively reducing global elevation errors through frame-by-frame constraints. The back-end employs an error state-based Kalman filter (ESKF) for GPS and IMU data fusion, enhancing the validity of true state estimation. A Scan Context loop closure detection method is designed, augmented by GPS detection as an auxiliary loop closure constraint to mitigate false loop closures. A global factor graph optimization model is also proposed. Experimental results demonstrate that, compared to existing open-source algorithms, the proposed method exhibits improved performance in structured urban scenes, reducing the average RMSE APE by 47.4% compared with LiDAR-only methods and by 22.9% compared with tightly coupled LiDAR-inertial methods. This work highlights the potential of multi-sensor fusion SLAM for achieving high-precision 3D localization and mapping in complex urban environments.<\/jats:p>","DOI":"10.3390\/jimaging12080345","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T16:15:00Z","timestamp":1785428100000},"page":"345","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-Sensor Fusion SLAM Based on LiDAR, IMU and GPS for Structured Urban Scenes"],"prefix":"10.3390","volume":"12","author":[{"given":"Jiajia","family":"Lu","sequence":"first","affiliation":[{"name":"School of Internet of Things Engineering, Wuxi University, Wuxi 214105, China"},{"name":"School of Computer Science, School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yue","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Engineering Research Center of Hyperconvergence Application and Security of loT Devices, Wuxi 214000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Engineering Research Center of Hyperconvergence Application and Security of loT Devices, Wuxi 214000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyang","family":"Ke","sequence":"additional","affiliation":[{"name":"School of Computer Science, School of Software, Nanjing University of Information Science and Technology, Nanjing 210044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1109\/MRA.2006.1678144","article-title":"Simultaneous localization and mapping: Part I","volume":"13","author":"Bailey","year":"2006","journal-title":"IEEE Robot. Autom. Mag."},{"key":"ref_2","first-page":"541","article-title":"Simultaneous localization and mapping","volume":"13","author":"Thrun","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1309","DOI":"10.1109\/TRO.2016.2624754","article-title":"Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age","volume":"32","author":"Cadena","year":"2016","journal-title":"IEEE Trans. Robot."},{"key":"ref_4","first-page":"1","article-title":"LOAM: LiDAR Odometry and Mapping in Real-Time","volume":"2","author":"Zhang","year":"2014","journal-title":"Robot. Sci. Syst. (RSS)"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/TIM.2013.2255977","article-title":"A wireless micro inertial measurement unit (IMU)","volume":"62","author":"Zhang","year":"2013","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TRO.2016.2597321","article-title":"On-Manifold Preintegration for Real-Time Visual\u2013Inertial Odometry","volume":"33","author":"Forster","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6557","DOI":"10.1109\/TVT.2019.2914477","article-title":"Covert spoofing algorithm of UAV based on GPS\/INS-integrated navigation","volume":"68","author":"Guo","year":"2019","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10514-020-09948-3","article-title":"S4-SLAM: A real-time 3D LIDAR SLAM system for ground\/watersurface multi-scene outdoor applications","volume":"45","author":"Zhou","year":"2021","journal-title":"Auton. Robots"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, B., Guan, Z., Li, B., Wen, G., and Zhao, Y. (2021). Research on SLAM algorithm and navigation of mobile robot based on ROS. 2021 IEEE International Conference on Mechatronics and Automation (ICMA), IEEE.","DOI":"10.1109\/ICMA52036.2021.9512584"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018). LeGO-LOAM: Lightweight and Ground-Optimized LiDAR Odometry and Mapping on Variable Terrain. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016). Real-Time Loop Closure in 2D LIDAR SLAM. 2016 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1729881419841532","DOI":"10.1177\/1729881419841532","article-title":"A Portable 3D LiDAR-Based System for Long-Term and Wide-Area People Behavior Measurement","volume":"16","author":"Koide","year":"2019","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Pan, Y., Xiao, P., He, Y., Shao, Z., and Li, Z. (2021). MULLS: Versatile LiDAR SLAM via Multi-Metric Linear Least Square. 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA48506.2021.9561364"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Behley, J., and Stachniss, C. (2018, January 26\u201330). Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments. Proceedings of the Robotics: Science and Systems (RSS), Pittsburgh, PA, USA.","DOI":"10.15607\/RSS.2018.XIV.016"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3-D Shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI)"},{"key":"ref_16","unstructured":"Segal, A., Haehnel, D., and Thrun, S. (July, January 28). Generalized-ICP. Proceedings of the Robotics: Science and Systems (RSS), Seattle, WA, USA."},{"key":"ref_17","unstructured":"Magnusson, M. (2009). The Three-Dimensional Normal-Distributions Transform\u2014An Efficient Representation for Registration, Surface Analysis, and Loop Detection. [Ph.D. Thesis, \u00d6rebro University]."},{"key":"ref_18","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. IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS45743.2020.9341176"},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","first-page":"2200459","DOI":"10.1002\/aisy.202200459","article-title":"Point-LIO: Robust High-Bandwidth Light Detection and Ranging Inertial Odometry","volume":"5","author":"He","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"ref_22","unstructured":"Dellaert, F. (2012). Factor Graphs and GTSAM: A Hands-on Introduction, Georgia Institute of Technology. Georgia Institute of Technology Technical Report."},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Kim, G., and Kim, A. (2018). Scan Context: Egocentric Spatial Descriptor for Place Recognition within 3D Point Cloud Map. 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE.","DOI":"10.1109\/IROS.2018.8593953"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TRO.2021.3116424","article-title":"Scan Context++: Structural Place Recognition Robust to Rotation and Lateral Variations in Urban Environments","volume":"38","author":"Kim","year":"2022","journal-title":"IEEE Trans. Robot."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"6958","DOI":"10.1109\/LRA.2022.3178797","article-title":"OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition","volume":"7","author":"Ma","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1109\/LRA.2022.3221336","article-title":"BoW3D: Bag of Words for Real-Time Loop Closing in 3D LiDAR SLAM","volume":"8","author":"Cui","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1109\/LRA.2024.3354550","article-title":"LinK3D: Linear Keypoints Representation for 3D LiDAR Place Recognition","volume":"3","author":"Cui","year":"2024","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1109\/LRA.2023.3236571","article-title":"KISS-ICP: In Defense of Point-to-Point ICP\u2014Simple, Accurate, and Robust Registration If Done the Right Way","volume":"8","author":"Vizzo","year":"2023","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2015). Visual-LiDAR Odometry and Mapping: Low Drift, Robust, and Fast. IEEE International Conference on Robotics and Automation (ICRA), IEEE.","DOI":"10.1109\/ICRA.2015.7139486"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/345\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T04:20:50Z","timestamp":1785558050000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/8\/345"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,30]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["jimaging12080345"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12080345","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,30]]}}}