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.
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