{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T23:31:02Z","timestamp":1762299062435,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,21]],"date-time":"2019-07-21T00:00:00Z","timestamp":1563667200000},"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":["2018YFB0105103, 2017YFA0603104"],"award-info":[{"award-number":["2018YFB0105103, 2017YFA0603104"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1764261, 41801335, and 41871370"],"award-info":[{"award-number":["U1764261, 41801335, and 41871370"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012543","name":"Shanghai Science and Technology Development Foundation","doi-asserted-by":"publisher","award":["17DZ1100202, 16DZ1100701"],"award-info":[{"award-number":["17DZ1100202, 16DZ1100701"]}],"id":[{"id":"10.13039\/100012543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["22120180095"],"award-info":[{"award-number":["22120180095"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The high-definition map (HD-map) of road structures is crucial for the safe planning and control of autonomous vehicles. However, generating and updating such maps requires intensive manual work. Simultaneous localization and mapping (SLAM) is able to automatically build and update a map of the environment. Nevertheless, there is still a lack of SLAM method for generating vector-based road structure maps. In this paper, we propose a vector-based SLAM method for the road structure mapping using vehicle-mounted multibeam LiDAR. We propose using polylines as the primary mapping element instead of grid maps or point clouds because the vector-based representation is lightweight and precise. We explored the following: (1) the extraction and vectorization of road structures based on multiframe probabilistic fusion; (2) the efficient vector-based matching between frames of road structures; (3) the loop closure and optimization based on the pose-graph; and (4) the global reconstruction of the vector map. One specific road structure, the road boundary, is taken as an example. We applied the proposed mapping method to three road scenes, ranging from hundreds of meters to over ten kilometers and the results are automatically generated vector-based road boundary maps. The average absolute pose error of the trajectory in the mapping is 1.83 m without the aid of high-precision GPS.<\/jats:p>","DOI":"10.3390\/rs11141726","type":"journal-article","created":{"date-parts":[[2019,7,22]],"date-time":"2019-07-22T02:55:37Z","timestamp":1563764137000},"page":"1726","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Automatic Vector-Based Road Structure Mapping Using Multibeam LiDAR"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7864-3255","authenticated-orcid":false,"given":"Junqiao","family":"Zhao","sequence":"first","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"},{"name":"The Institute of Intelligent Vehicles, Tongji University, Shanghai 201804, China"}]},{"given":"Xudong","family":"He","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"},{"name":"The Institute of Intelligent Vehicles, Tongji University, Shanghai 201804, China"}]},{"given":"Jun","family":"Li","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"},{"name":"The Institute of Intelligent Vehicles, Tongji University, Shanghai 201804, China"}]},{"given":"Tiantian","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Surveying and Geo-Informatics, Tongji University, Shanghai 201804, China"}]},{"given":"Chen","family":"Ye","sequence":"additional","affiliation":[{"name":"MOE Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science and Technology, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China"}]},{"given":"Lu","family":"Xiong","sequence":"additional","affiliation":[{"name":"The Institute of Intelligent Vehicles, Tongji University, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/J.ENG.2016.02.010","article-title":"Autonomous Driving in the iCity\u2014HD Maps as a Key Challenge of the Automotive Industry","volume":"2","author":"Seif","year":"2016","journal-title":"Engineering"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1109\/MITS.2014.2364081","article-title":"Generation of Accurate Lane-Level Maps from Coarse Prior Maps and Lidar","volume":"7","author":"Joshi","year":"2015","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhao, J., Ye, C., Wu, Y., Guan, L., Cai, L., Sun, L., Yang, T., He, X., Li, J., and Ding, Y. (2018, January 4\u20137). TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569629"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Bender, P., Ziegler, J., and Stiller, C. (2014, January 8\u201311). Lanelets: Efficient map representation for autonomous driving. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Ypsilanti, MI, USA.","DOI":"10.1109\/IVS.2014.6856487"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MITS.2014.2360306","article-title":"Experience, Results and Lessons Learned from Automated Driving on Germany\u2019s Highways","volume":"7","author":"Aeberhard","year":"2015","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_6","first-page":"1147","article-title":"ORB-SLAM: A Versatile and Accurate Monocular SLAM System","volume":"31","author":"Montiel","year":"2017","journal-title":"IEEE Trans. Robot."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Engel, J., Sch\u00f6ps, T., and Cremers, D. (2014, January 6\u201312). LSD-SLAM: Large-scale direct monocular SLAM. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10605-2_54"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hata, A., and Wolf, D. (2014, January 8\u201311). Road marking detection using LIDAR reflective intensity data and its application to vehicle localization. Proceedings of the IEEE International Conference on Intelligent Transportation Systems, Qingdao, China.","DOI":"10.1109\/ITSC.2014.6957753"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jeong, J., Cho, Y., and Kim, A. (2017, January 11\u201314). Road-SLAM: Road marking based SLAM with lane-level accuracy. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2017.7995958"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Singh, S. (2014, January 12\u201316). LOAM: Lidar Odometry and Mapping in Real-time. Proceedings of the Robotics: Science and Systems, Berkeley, CA, USA.","DOI":"10.15607\/RSS.2014.X.007"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hess, W., Kohler, D., Rapp, H., and Andor, D. (2016, January 16\u201321). Real-time loop closure in 2D LIDAR SLAM. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Stockholm, Sweeden.","DOI":"10.1109\/ICRA.2016.7487258"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/TITS.2015.2477817","article-title":"Feature Detection for Vehicle Localization in Urban Environments Using a Multilayer LIDAR","volume":"17","author":"Hata","year":"2016","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s10846-009-9313-2","article-title":"VecSLAM: An Efficient Vector-Based SLAM Algorithm for Indoor Environments","volume":"56","author":"Sohn","year":"2009","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_14","first-page":"12","article-title":"A Light-and-Fast SLAM Algorithm for Robots in Indoor Environments Using Line Segment Map","volume":"2011","author":"Kuo","year":"2011","journal-title":"J. Robot."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"22","DOI":"10.14311\/APP.2015.1.0022","article-title":"Vector maps in mobile robotics","volume":"2","author":"Jelinek","year":"2015","journal-title":"Acta Polytech. Ctu Proc."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Dichtl, J., Le, X.S., Lozenguez, G., Fabresse, L., and Bouraqadi, N. (2019, January 24\u201326). PolySLAM: A 2D Polygon-based SLAM Algorithm. Proceedings of the IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), Porto-Gondomar, Portugal.","DOI":"10.1109\/ICARSC.2019.8733647"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, X., Zhao, J., Sun, L., Huang, Y., Zhang, X., Li, J., and Ye, C. (2018, January 27\u201330). Automatic Vector-based Road Structure Mapping Using Multi-beam LiDAR. Proceedings of the IEEE International Conference on Intelligent Transportation Systems (ITSC), Auckland, New Zealand.","DOI":"10.1109\/ITSC.2018.8569894"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Dichtl, J., and Luc, F. (2018). PolyMap: A 2D Polygon-Based Map Format for Multi-robot Autonomous Indoor Localization and Mapping. Intelligent Robotics and Applications, Springer.","DOI":"10.1007\/978-3-319-97586-3_11"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1109\/TITS.2011.2173196","article-title":"A Novel Lane Detection System With Efficient Ground Truth Generation","volume":"13","author":"Borkar","year":"2012","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hata, A.Y., Osorio, F.S., and Wolf, D.F. (2014, January 8\u201311). Robust curb detection and vehicle localization in urban environments. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Ypsilanti, MI, USA.","DOI":"10.1109\/IVS.2014.6856405"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1109\/TITS.2018.2789462","article-title":"Road-Segmentation-Based Curb Detection Method for Self-Driving via a 3D-LiDAR Sensor","volume":"19","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Caltagirone, L., Scheidegger, S., Svensson, L., and Wahde, M. (2017, January 11\u201314). Fast LIDAR-based road detection using fully convolutional neural networks. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Redondo Beach, CA, USA.","DOI":"10.1109\/IVS.2017.7995848"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct Sparse Odometry","volume":"40","author":"Engel","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Olson, E.B. (2009, January 12\u201317). Real-time correlative scan matching. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Kobe, Japan.","DOI":"10.1109\/ROBOT.2009.5152375"},{"key":"ref_27","unstructured":"Rusinkiewicz, S., and Levoy, M. (June, January 28). Efficient variants of the ICP algorithm. Proceedings of the Third International Conference on 3-D Digital Imaging and Modeling, Quebec City, QC, Canada."},{"key":"ref_28","unstructured":"Hahnel, D., Burgard, W., Fox, D., and Thrun, S. (2003, January 27\u201331). A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_29","unstructured":"Biber, P., and Stra\u00dfer, W. (2003, January 27\u201331). The normal distributions transform: A new approach to laser scan matching. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Schreiber, M., Kn\u00f6ppel, C., and Franke, U. (2013, January 23\u201326). LaneLoc: Lane marking based localization using highly accurate maps. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gold Coast, Australia.","DOI":"10.1109\/IVS.2013.6629509"},{"key":"ref_31","first-page":"1961","article-title":"Parametric Correspondence and Chamfer Matching: Two New Techniques for Image Matching","volume":"2","author":"Barrow","year":"1977","journal-title":"Proc. Int. Jt. Conf. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Floros, G., Zander, B.V.D., and Leibe, B. (2013, January 6\u201310). OpenStreetSLAM: Global vehicle localization using OpenStreetMaps. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630703"},{"key":"ref_33","first-page":"53","article-title":"lCL: Iterative closest line A novel point cloud registration algorithm based on linear features","volume":"10","author":"Alshawa","year":"2007","journal-title":"Ekscentar"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Censi, A. (2008, January 19\u201323). An ICP variant using a point-to-line metric. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Pasadena, CA, USA.","DOI":"10.1109\/ROBOT.2008.4543181"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4517","DOI":"10.1109\/TVT.2016.2535210","article-title":"Generation of a Precise and Efficient Lane-Level Road Map for Intelligent Vehicle Systems","volume":"66","author":"Gwon","year":"2017","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2008.80","article-title":"Openstreetmap: User-generated street maps","volume":"7","author":"Haklay","year":"2008","journal-title":"IEEE Pervasive Comput."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"786","DOI":"10.1109\/TITS.2010.2050689","article-title":"Creating Enhanced Maps for Lane-Level Vehicle Navigation","volume":"11","author":"Betaille","year":"2010","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"925","DOI":"10.1109\/TITS.2013.2291395","article-title":"Generation of a precise roadway map for autonomous cars","volume":"15","author":"Jo","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.geomorph.2017.05.016","article-title":"Semi-automatic mapping of linear-trending bedforms using Self-Organizing Maps algorithm","volume":"293","author":"Foroutan","year":"2017","journal-title":"Geomorphology"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1002\/rob.20258","article-title":"The Stanford entry in the Urban Challenge","volume":"25","author":"Montemerlo","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1016\/j.imavis.2012.06.010","article-title":"A novel framework for making dominant point detection methods non-parametric","volume":"30","author":"Prasad","year":"2012","journal-title":"Image Vis. Comput."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A Method for Registration of 3D Shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","unstructured":"K\u00fcmmerle, R., Grisetti, G., Strasdat, H., Konolige, K., and Burgard, W. (2011, January 9\u201313). G2o: A general framework for graph optimization. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Shanghai, China."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.1111\/j.1467-8659.2011.02033.x","article-title":"An optimal transport approach to robust reconstruction and simplification of 2D shapes","volume":"30","author":"Alliez","year":"2011","journal-title":"Comput. Graph. Forum"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets Robotics: The KITTI Dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1177\/0278364911400640","article-title":"Ford campus vision and lidar data set","volume":"30","author":"Pandey","year":"2011","journal-title":"Int. J. Robot. Res."},{"key":"ref_47","unstructured":"Grupp, M. (2019, June 21). evo: Python Package for the Evaluation of Odometry and SLAM. Available online: https:\/\/github.com\/MichaelGrupp\/evo."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1016\/j.trpro.2016.05.434","article-title":"Analysis of the Pavement Surface Texture by 3D Scanner","volume":"14","author":"Kotek","year":"2016","journal-title":"Transp. Res. Procedia"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1726\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:07:59Z","timestamp":1760188079000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/14\/1726"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,21]]},"references-count":48,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11141726"],"URL":"https:\/\/doi.org\/10.3390\/rs11141726","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,7,21]]}}}