Rapid Update of Road Surface Databases Using Mobile LiDAR: Road-Markings

Road surface markings are used on paved roadways to provide guidance and information to drivers and pedestrians, which are a critical feature in the traffic management systems. This paper presents an automated approach to detection and extraction of road markings from mobile laser scanning (MLS) point clouds by taking advantages of multiple data features. To improve computational efficiency, the raw MLS point cloud data are first converted to geo-referenced images, based on elevation, intensity and point density, using inverse distance weighted interpolation, respectively. Afterwards, three filters are designed to extract road markings step-by-step: (1) the elevation filter is used to generate an elevation mask to remove high objects from the geo-referenced intensity image, (2) the point density filter is implemented to extract road surfaces in the geo-referenced intensity image, (3) the filtered geo-referenced intensity image is processed by thresholding and point density to obtain road markings, followed by a Canny detector and Hough transform used to extract straight-lines of road markings. Two RIEGL VMX-450 datasets demonstrate that the proposed multi-feature road marking extraction method has a good performance of road marking extraction from a large volume of mobile laser scanning data.