Automatic Registration of Mobile LiDAR Data Using High-Reflectivity Traffic Signs
Publication: Journal of Construction Engineering and Management
Volume 142, Issue 8
Abstract
Mobile light detection and ranging (LiDAR) is currently one of the most used instruments in road inspections to collect geometric and radiometric data. Different strips of the same area are common and bring problems in data registration related to the inaccuracies of global navigation satellite systems. This paper shows a methodology for automatic and robust data registration. It is based on segmentation of highly reflective data (i.e., road signs) and avoids the use of control points manually marked, typically used in the traditional techniques. The data set must be acquired under favorable climatological conditions. Root-mean-square errors in registration show values around 2 cm, while if the high-reflectivity segmentation is not used values higher than 8 cm are obtained.
Get full access to this article
View all available purchase options and get full access to this article.
Acknowledgments
The authors want to thank the Xunta de Galicia (Grant No. CN2012/269) and Spanish Government (Grant No. TIN2013-46801-C4-4-R; ENE2013-48015-C3-1-R; FPU: AP2010-2969).
References
González-Jorge, H., Riveiro, B., Armesto, J., and Arias, P. (2013). “Evaluation of road signs using radiometric and geometric data from terrestrial LiDAR.” Opt. Appl., 43(3), 421–433.
Gressin, A., Mallet, C., Demantké, J., and David, N. (2013). “Towards 3D LiDAR point cloud registration improvement using optimal neighborhood knowledge.” ISPRS J. Photogramm. Remote Sens., 79, 240–251.
He, G. Z., and Yang, J. (2013). “Deformation monitoring for subway tunnels based on TLS.” Adv. Mater. Res., 864–867, 2744–2749.
Kavanagh, R. M. (2007). “Gyroscopes for orientation and inertial navigation systems.” Kartografija I Geoinformacije, 6, 254–271.
Laneurit, J., Blanc, C., Chapuis, R., and Trassoudaine, L. (2003). “Multisensorial data fusion for global vehicle and obstacles absolute positioning.” IEEE Intelligent Vehicles Symp., IEEE, 138–143.
Liao, Y., Xu, F., Zhao, Z., and Hagiawara, I. (2014). “A point cloud registration method based on point cloud region and applications samples.” Commun. Comput. Inform. Sci., 474, 216–227.
Mahboob Kanafi, M., Kuosmanen, A., Pellinen, T. K., and Tuononen, A. J. (2014). “Macro- and micro-texture evolution of road pavements and correlation with friction.” Int. J. Pavement Eng., 16(2), 168–179.
MATLAB [Computer software]. MathWorks, Natick, MA.
McElhinney, C., Kumar, P., Cahalan, C., and McCarthy, T. (2010). “Initial results from the European road safety inspections (EURSI) mobile mapping project.” ISPRS J. Photogramm. Remote Sens., XXXVII(5), 440–445.
Merlo, S., Norgia, M., and Donati, S. (2000). “Fiber gyroscope principles Handbook.” Fiber Optics Sens. Technol., 16, 1–23.
Petri, G. (2010). “Mobile mapping systems: An introduction to the technology.” Geoinformatics, 13(1), 32–43.
Puente, I., González-Jorge, H., Martínez-Sánchez, J., and Arias, P. (2013a). “Review of mobile mapping and surveying technologies.” Measurement, 46(7), 2127–2145.
Puente, I., González-Jorge, H., Riveiro, B., and Arias, P. (2013b). “Accuracy verification of the Lynx Mobile Mapper system.” Opt. Laser Technol., 45(1), 578–586.
Puente, I., Solla, M., González-Jorge, H., and Arias, P. (2013c). “Validation of mobile LiDAR surveying for measuring pavement layer thicknesses and volumes.” NDT&E Int., 60, 70–76.
Rabbani, T., Dijkman, S., van den Heuvel, F., and Vosselman, G. (2007). “An integrated approach for modelling and global registration of point clouds.” ISPRS J. Photogramm. Remote Sens., 61(6), 355–370.
Solla, M., Lagüela, S., González-Jorge, H., and Arias, P. (2014). “Approach to identify cracking in asphalt pavement using GPR and infrared thermographic methods.” NDT&E Int., 62, 55–65.
Tang, J., Chen, Y., Jaakkola, A., Liu, J., Hyyppä, J., and Hyyppä, H. (2014). “NAVIS-An UGV indoor positioning system using laser scan matching for large area real-time applications.” Sensors, 14(7), 11805–11824.
Varela-González, M., Solla, M., Martínez-Sánchez, J., and Arias, P. (2014). “A semi-automatic processing and visualization tool for ground-penetrating radar pavement thickness data.” Autom. Constr., 45, 42–49.
Wang, Y., Chen, S. Y., Zhang, Y. C., Chen, H., Guo, P., and Yang, J. (2013). “Automatic road extraction for airborne LiDAR data.” Proc. SPIE, 8905, 890528.
Information & Authors
Information
Published In
Copyright
© 2016 American Society of Civil Engineers.
History
Received: Sep 11, 2015
Accepted: Dec 9, 2015
Published online: Feb 16, 2016
Discussion open until: Jul 16, 2016
Published in print: Aug 1, 2016
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.