@conference {Mancini2012281,
	title = {Automatic road object extraction from Mobile Mapping Systems},
	booktitle = {Proceedings of 2012 8th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, MESA 2012},
	year = {2012},
	note = {cited By 3},
	pages = {281-286},
	abstract = {Mobile Mapping Systems (MMSs) often represent the best choice to provide an accurate 3D modeling of the environment, especially in urban streets where the aerial/satellite surveys do not provide accurate data. MMSs are equipped with many kinds of sensors, and, in particular, laser scanners that allow 2D/3D environment modeling from very dense point clouds. Usually an operator manually explores the point cloud to discover and mark a particular feature of interest (e.g., road line, cross-walk). Obviously this procedure is tedious and expensive. One of the greater challenges is to automatically extract objects/features from co-registered data coming from LiDAR, optical and positioning sensors. This paper presents an automatic feature/object approach to extract and then to georeference with high accuracy/precision horizontal road signs, mainly lanes and crosswalks. The proposed approach exploits image processing techniques and methods for the 3D to 2D re-projection of data. The results obtained demonstrate that is possible to achieve accuracy and precision in the range of one centimeter. {\textcopyright} 2012 IEEE.},
	doi = {10.1109/MESA.2012.6275575},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84867444429\&partnerID=40\&md5=bbdfcd587f93e0fdf60be30447684e89},
	author = {Adriano Mancini and Emanuele Frontoni and Primo Zingaretti}
}
