{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T05:55:43Z","timestamp":1784872543030,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T00:00:00Z","timestamp":1708905600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Project of China","award":["2022YFB4300400"],"award-info":[{"award-number":["2022YFB4300400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In response to the demand for high-precision point cloud mapping of subway trains in long tunnel degradation scenarios in major urban cities, we propose a map construction method based on LiDAR and inertial measurement sensors. This method comprises a tightly coupled frontend odometry system based on error Kalman filters and backend optimization using factor graphs. In the frontend odometry, inertial calculation results serve as predictions for the filter, and residuals between LiDAR points and local map plane point clouds are used for filter updates. The global pose graph is constructed based on inter-frame odometry and other constraint factors, followed by a smoothing optimization for map building. Multiple experiments in subway tunnel scenarios demonstrate that the proposed method achieves robust trajectory estimation in long tunnel scenes, where classical multi-sensor fusion methods fail due to sensor degradation. The proposed method achieves a trajectory consistency of 0.1 m in tunnel scenes, meeting the accuracy requirements for train arrival, parking, and interval operations. Additionally, in an industrial park scenario, the method is compared with ground truth provided by inertial navigation, showing an accumulated error of less than 0.2%, indicating high precision.<\/jats:p>","DOI":"10.3390\/rs16050809","type":"journal-article","created":{"date-parts":[[2024,2,26]],"date-time":"2024-02-26T10:40:17Z","timestamp":1708944017000},"page":"809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["High-Precision Map Construction in Degraded Long Tunnel Environments of Urban Subways"],"prefix":"10.3390","volume":"16","author":[{"given":"Cheng","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Rail Transit, Tongji University, Shanghai 201804, China"},{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5527-2914","authenticated-orcid":false,"given":"Wenbo","family":"Pan","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwen","family":"Yuan","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenyu","family":"Huang","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Yuan","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quandong","family":"Wang","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fuyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sun, H., Xu, Z., Yao, L., Zhong, R., Du, L., and Wu, H. (2020). Tunnel monitoring and measuring system using mobile laser scanning: Design and deployment. Remote Sens., 12.","DOI":"10.3390\/rs12040730"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"01005","DOI":"10.1051\/matecconf\/201929501005","article-title":"ARCHITA: An innovative multidimensional mobile mapping system for tunnels and infrastructures","volume":"295","author":"Foria","year":"2019","journal-title":"MATEC Web Conf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2071","DOI":"10.1109\/TITS.2016.2633344","article-title":"Intelligent localization of a high-speed train using LSSVM and the online sparse optimization approach","volume":"18","author":"Cheng","year":"2017","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"814","DOI":"10.1109\/TITS.2016.2590579","article-title":"Vulnerabilities, attacks, and countermeasures in balise-based train control systems","volume":"18","author":"Wu","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"4603","DOI":"10.1109\/TITS.2020.3046497","article-title":"A train positioning method based-on vision and millimeter-wave radar data fusion","volume":"23","author":"Wang","year":"2021","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TIM.2018.2838799","article-title":"Evaluation of experimental GNSS and 10-DOF MEMS IMU measurements for train positioning","volume":"68","author":"Otegui","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Buffi, A., and Nepa, P. (2017, January 9\u201311). An RFID-based technique for train localization with passive tags. Proceedings of the IEEE International Conference on RFID (RFID), Phoenix, AZ, USA.","DOI":"10.1109\/RFID.2017.7945602"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Daoust, T., Pomerleau, F., and Barfoot, T.D. (2016, January 1\u20133). Light at the end of the tunnel: High-speed lidar-based train localization in challenging underground environments. Proceedings of the 2016 13th Conference on Computer and Robot Vision (CRV), Victoria, BC, Canada.","DOI":"10.1109\/CRV.2016.54"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, H., Pan, W., Hu, Y., Li, C., Yuan, X., and Long, T. (2022). A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks. Sensors, 22.","DOI":"10.3390\/s22165989"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pan, W., Fan, X., Li, H., and He, K. (2023). Long-Range Perception System for Road Boundaries and Objects Detection in Trains. Remote Sens., 15.","DOI":"10.3390\/rs15143473"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"112661","DOI":"10.1016\/j.measurement.2023.112661","article-title":"Barometer assisted smartphone localization for vehicle navigation in multilayer road networks","volume":"211","author":"Wang","year":"2023","journal-title":"Measurement"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1002\/rob.21842","article-title":"Flying on point clouds: Online trajectory generation and autonomous navigation for quadrotors in cluttered environments","volume":"36","author":"Gao","year":"2019","journal-title":"J. Field Robot."},{"key":"ref_13","first-page":"2505512","article-title":"A Novel Deep Odometry Network for Vehicle Positioning Based on Smartphone","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ye, H., Chen, Y., and Liu, M. (2019, January 20\u201324). Tightly coupled 3d lidar inertial odometry and mapping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada.","DOI":"10.1109\/ICRA.2019.8793511"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhao, S., Fang, Z., Li, H., and Scherer, S. (2019, January 4\u20138). A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments. Proceedings of the 2019 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China.","DOI":"10.1109\/IROS40897.2019.8967880"},{"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","first-page":"2036","DOI":"10.1109\/LRA.2021.3061387","article-title":"Extrinsic calibration of multiple lidars of small fov in targetless environments","volume":"6","author":"Liu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"3184","DOI":"10.1109\/LRA.2021.3062815","article-title":"BALM: Bundle adjustment for lidar mapping","volume":"6","author":"Liu","year":"2021","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Lin, J., and Zhang, F. (August, January 31). Loam livox: A fast, robust, high-precision lidar odometry and mapping package for lidars of small fov. Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France.","DOI":"10.1109\/ICRA40945.2020.9197440"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lin, J., and Zhang, F. (2022, January 23\u201327). R 3 LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Philadelphia, PA, USA.","DOI":"10.1109\/ICRA46639.2022.9811935"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"106125","DOI":"10.1016\/j.engappai.2023.106125","article-title":"A review of high-definition map creation methods for autonomous driving","volume":"122","author":"Bao","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3344","DOI":"10.1109\/JSEN.2020.3023738","article-title":"Uwb\/lidar coordinate matching method with anti-degeneration capability","volume":"21","author":"Zhou","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.inffus.2023.01.025","article-title":"Multi-sensor integrated navigation\/positioning systems using data fusion: From analytics-based to learning-based approaches","volume":"95","author":"Zhuang","year":"2023","journal-title":"Inf. Fusion"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Heirich, O., Robertson, P., and Strang, T. (2013, January 6\u201310). RailSLAM\u2014Localization of Rail Vehicles and Mapping of Geometric Railway Tracks. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631322"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"9818","DOI":"10.1109\/LRA.2022.3190093","article-title":"Rail Vehicle Localization and Mapping with LiDAR-Vision-Inertial-GNSS Fusion","volume":"7","author":"Wang","year":"2022","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1815","DOI":"10.1109\/LRA.2019.2897169","article-title":"Experimental comparison of visual-aided odometry methods for rail vehicles","volume":"4","author":"Tschopp","year":"2019","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_28","unstructured":"Wang, Y., Song, W., Lou, Y., Huang, F., Tu, Z., and Zhang, S. (2021). Simultaneous Location of Rail Vehicles and Mapping of Environment with Multiple LiDARs. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Shan, T., and Englot, B. (2018, January 1\u20135). LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8594299"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Fan, X., Chen, Z., Liu, P., and Pan, W. (2023). Simultaneous Vehicle Localization and Roadside Tree Inventory Using Integrated LiDAR-Inertial-GNSS System. Remote Sens., 15.","DOI":"10.3390\/rs15205057"},{"key":"ref_32","unstructured":"Feng, C., Taguchi, Y., and Kamat, V.R. (June, January 31). Fast plane extraction in organized point clouds using agglomerative hierarchical clustering. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1109\/LRA.2020.3028828","article-title":"Interactive 3D graph SLAM for map correction","volume":"6","author":"Koide","year":"2020","journal-title":"IEEE Robot. Autom. Lett."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/809\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:04:52Z","timestamp":1760105092000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/5\/809"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,26]]},"references-count":33,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["rs16050809"],"URL":"https:\/\/doi.org\/10.3390\/rs16050809","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,26]]}}}