{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T09:16:36Z","timestamp":1773393396979,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,6,5]],"date-time":"2019-06-05T00:00:00Z","timestamp":1559692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Light Detection and Ranging (LiDAR) sensors are being considered as new traffic infrastructure sensors to detect road users\u2019 trajectories for connected\/autonomous vehicles and other traffic engineering applications. A LiDAR-enhanced traffic infrastructure system requires multiple LiDAR sensors around intersections, along with road segments, which can provide a seamless detection range at intersections or along arterials. Each LiDAR sensor generates cloud points of surrounding objects in a local coordinate system with the sensor at the origin, so it is necessary to integrate multiple roadside LiDAR sensors\u2019 data into the same coordinate system. None of existing methods can integrate the data from roadside LiDAR sensors, because the extensive detection range of roadside sensors generates low-density cloud points and the alignment of roadside sensors is different from mapping scans or autonomous sensing systems. This paper presents a method to register datasets from multiple roadside LiDAR sensors. This approach innovatively integrates LiDAR datasets with 3D cloud points of road surface and 2D reference point features, so the method is abbreviated as RGP (Registration with Ground and Points). The RGP method applies optimization algorithms to identify the optimized linear coordinate transformation. This research considered the genetic algorithm (global optimization) and the hill climbing algorithm (local optimization). The performance of the RGP method and the different optimization algorithms was evaluated with field LiDAR sensors data. When the developed process can integrate data from roadside sensors, it can also register LiDAR sensors\u2019 data on an autonomous vehicle or a robot.<\/jats:p>","DOI":"10.3390\/rs11111354","type":"journal-article","created":{"date-parts":[[2019,6,6]],"date-time":"2019-06-06T03:38:01Z","timestamp":1559792281000},"page":"1354","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Data Registration with Ground Points for Roadside LiDAR Sensors"],"prefix":"10.3390","volume":"11","author":[{"given":"Rui","family":"Yue","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-4540","authenticated-orcid":false,"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"given":"Jianqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}]},{"given":"Renjuan","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250002, China"}]},{"given":"Changwei","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Economics and Management, Chang\u2019An University, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1206","DOI":"10.1109\/ACCESS.2015.2461602","article-title":"A survey of 5G network: Architecture and emerging technologies","volume":"3","author":"Gupta","year":"2015","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1162","DOI":"10.1109\/JPROC.2011.2132790","article-title":"Dedicated short-range communications (DSRC) standards in the United States","volume":"99","author":"Kenney","year":"2011","journal-title":"Proc. IEEE"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1109\/MCOM.2006.1580935","article-title":"Vehicle-to-vehicle wireless communication protocols for enhancing highway traffic safety","volume":"44","author":"Biswas","year":"2006","journal-title":"IEEE Commun. Mag."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.optlastec.2019.02.039","article-title":"Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm","volume":"115","author":"Wu","year":"2019","journal-title":"Opt. Laser Technol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.trc.2019.01.007","article-title":"Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors","volume":"100","author":"Zhao","year":"2019","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wu, J., Xu, H., and Zheng, J. (2017, January 16\u201319). Automatic Background Filtering and Lane Identification with Roadside LiDAR Data. Proceedings of the IEEE 20th International Conference on Intelligent Transportation, Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317723"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1177\/0361198118775839","article-title":"3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data","volume":"2672","author":"Sun","year":"2019","journal-title":"Transp. Res. Rec."},{"key":"ref_8","first-page":"32","article-title":"An Automatic Procedure for Vehicle Tracking with a Roadside LiDAR Sensor","volume":"88","author":"Wu","year":"2018","journal-title":"ITE J."},{"key":"ref_9","unstructured":"Zheng, Y., Xu, H., Tian, Z., and Wu, J. (2018, January 7\u201311). Design and Implementation of the DSRC Bluetooth Communication and Mobile Application with LiDAR Sensor. Proceedings of the 97th Transportation Research Board Annual Meeting, Washington, DC, USA."},{"key":"ref_10","unstructured":"Zhao, J., Xu, H., Wu, D., and Liu, H. (2018, January 7\u201311). An Artificial Neural Network to Identify Pedestrians and Vehicles from Roadside 360-Degree LiDAR Data. Proceedings of the 97th Transportation Research Board Annual Meeting, Washington, DC, USA."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105571","DOI":"10.1016\/j.optlastec.2019.105571","article-title":"Revolution and rotation-based method for roadside LiDAR data integration","volume":"119","author":"Lv","year":"2019","journal-title":"Opt. Laser Technol."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gebre, B.A., Men, H., and Pochiraju, K. (2009, January 9\u201310). Remotely operated and autonomous mapping system (ROAMS). Proceedings of the IEEE International Conference on Technologies for Practical Robot Applications, Woburn, MA, USA.","DOI":"10.1109\/TEPRA.2009.5339624"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Liu, S., Tong, X., Chen, J., Liu, X., Sun, W., Xie, H., Chen, P., Jin, Y., and Ye, Z. (2016). A linear feature-based approach for the registration of unmanned aerial vehicle remotely-sensed images and airborne LiDAR data. Remote Sens., 8.","DOI":"10.3390\/rs8020082"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1038\/nphoton.2010.148","article-title":"LIDAR: Mapping the world in 3D","volume":"4","author":"Schwarz","year":"2010","journal-title":"Nat. Photonics"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1016\/j.optlastec.2012.06.015","article-title":"3D shape modeling using a self-developed hand-held 3D laser scanner and an efficient HT-ICP point cloud registration algorithm","volume":"45","author":"Chen","year":"2013","journal-title":"Opt. Laser Technol."},{"key":"ref_16","first-page":"303","article-title":"Feature-based registration of terrestrial lidar point clouds","volume":"37","author":"Jaw","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_17","first-page":"586","article-title":"Method for registration of 3-D shapes","volume":"Volume 1611","author":"Besl","year":"1992","journal-title":"Sensor Fusion IV: Control Paradigms and Data Structures"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wu, J., Xu, H., and Liu, W. (2019). Points Registration for Roadside LiDAR Sensors. Transp. Res. Rec., in press.","DOI":"10.1177\/0361198119843855"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Turk, G., and Levoy, M. (1994, January 24\u201329). Zippered polygon meshes from range images. Proceedings of the 21st Annual Conference on Computer Graphics and Interactive Techniques, Orlando, FL, USA.","DOI":"10.1145\/192161.192241"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Masuda, T., Sakaue, K., and Yokoya, N. (1996, January 25\u201319). Registration and integration of multiple range images for 3-D model construction. Proceedings of the 13th International Conference on Pattern Recognition, Vienna, Austria.","DOI":"10.1109\/ICPR.1996.546150"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1117\/12.189139","article-title":"Three-dimensional registration using range and intensity information","volume":"Volume 2350","author":"Godin","year":"1994","journal-title":"Videometrics III"},{"key":"ref_22","unstructured":"Jost, T., and Hugli, H. (2002, January 19\u201321). A multi-resolution scheme ICP algorithm for fast shape registration. Proceedings of the First International Symposium on 3D Data Processing Visualization and Transmission, Padova, Italy."},{"key":"ref_23","unstructured":"Gelfand, N. (2006). Feature Analysis and Registration of Scanned Surfaces, Stanford University."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1006\/cviu.2002.0963","article-title":"Geometry and texture recovery of scenes of large scale","volume":"88","author":"Stamos","year":"2002","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_25","unstructured":"Stamos, I., and Leordeanu, M. (2003, January 16\u201322). Automated feature-based range registration of urban scenes of large scale. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Madison, WI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"699","DOI":"10.14358\/PERS.71.6.699","article-title":"Photogrammetric and LiDAR data registration using linear features","volume":"71","author":"Habib","year":"2005","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","unstructured":"Akca, D. (2003). Full Automatic Registration of Laser Scanner Points Clouds, ETH Zurich."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.isprsjprs.2006.09.006","article-title":"An integrated approach for modelling and global registration of point clouds","volume":"61","author":"Rabbani","year":"2007","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","unstructured":"Von Hansen, W. (2007). Registration of Agia Sanmarina LIDAR data using surface elements. Proceedings of the ISPRS Workshop on Laser Scanning, ISPRS."},{"key":"ref_30","unstructured":"Bodensteiner, C., and Arens, M. (2012, January 11\u201315). Real-time 2D video\/3D LiDAR registration. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Giering, M., Venugopalan, V., and Reddy, K. (2015, January 15\u201317). Multi-modal sensor registration for vehicle perception via deep neural networks. Proceedings of the IEEE High Performance Extreme Computing Conference (HPEC), Waltham, MA, USA.","DOI":"10.1109\/HPEC.2015.7322485"},{"key":"ref_32","unstructured":"Cho, H., Seo, Y.W., Kumar, B.V., and Rajkumar, R.R. (June, January 31). A multi-sensor fusion system for moving object detection and tracking in urban driving environments. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Hong Kong, China."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1002\/rob.21430","article-title":"Moving object detection with laser scanners","volume":"30","author":"Mertz","year":"2013","journal-title":"J. Field Robot."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, J., Xu, H., Lv, B., Yue, R., and Li, Y. (2019). Automatic Ground Points Identification Method for Roadside LiDAR Data. Transp. Res. Rec., 2673.","DOI":"10.1177\/0361198119843869"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.aap.2018.09.001","article-title":"A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data","volume":"121","author":"Wu","year":"2018","journal-title":"Accid. Anal. Prev."},{"key":"ref_36","unstructured":"Golberg, D.E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning, Addion Wesley."},{"key":"ref_37","unstructured":"Russell, S.J., and Norvig, P. (2016). Artificial Intelligence: A Modern Approach, Pearson Education Limited."},{"key":"ref_38","unstructured":"Weise, T. (2019, April 07). Global Optimization Algorithms-Theory and Application. Available online: http:\/\/www.it-weise.de\/projects\/book.pdf."},{"key":"ref_39","unstructured":"(2019, May 07). Genetic Algorithm and Direct Search Toolbox\u2122 2 User\u2019s Guide. Available online: https:\/\/laboratoriomatematicas.uniandes.edu.co\/metodos\/contenido\/contenido\/ag.pdf."},{"key":"ref_40","first-page":"1","article-title":"A genetic algorithm for function optimization: A Matlab implementation","volume":"95","author":"Houck","year":"1995","journal-title":"Ncsu-ie tr"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"53878","DOI":"10.1109\/ACCESS.2019.2912581","article-title":"A Decision Tree based Road Recognition Approach using Roadside Fixed 3D LiDAR Sensors","volume":"7","author":"Zheng","year":"2019","journal-title":"IEEE Access"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Cui, Y., Xu, H., Wu, J., Sun, Y., and Zhao, J. (2019). Automatic Vehicle Tracking with Roadside LiDAR Data for the Connected-Vehicles System. IEEE Intell. Syst., in press.","DOI":"10.1109\/MIS.2019.2918115"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"65944","DOI":"10.1109\/ACCESS.2019.2916718","article-title":"Deer Crossing Road Detection with Roadside LiDAR Sensor","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1177\/0361198118775841","article-title":"Automatic background filtering method for roadside LiDAR data","volume":"2672","author":"Wu","year":"2018","journal-title":"Transp. Res. Rec."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1354\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:56:18Z","timestamp":1760187378000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/11\/1354"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,5]]},"references-count":44,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2019,6]]}},"alternative-id":["rs11111354"],"URL":"https:\/\/doi.org\/10.3390\/rs11111354","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,5]]}}}