Abstract
Abstract. Road markings as critical feature in high-defination maps, which are Advanced Driver Assistance System (ADAS) and self-driving technology required, have important functions in providing guidance and information to moving cars. Mobile laser scanning (MLS) system is an effective way to obtain the 3D information of the road surface, including road markings, at highway speeds and at less than traditional survey costs. This paper presents a novel method to automatically extract road markings from MLS point clouds. Ground points are first filtered from raw input point clouds using neighborhood elevation consistency method. The basic assumption of the method is that the road surface is smooth. Points with small elevation-difference between neighborhood are considered to be ground points. Then ground points are partitioned into a set of profiles according to trajectory data. The intensity histogram of points in each profile is generated to find intensity jumps in certain threshold which inversely to laser distance. The separated points are used as seed points to region grow based on intensity so as to obtain road mark of integrity. We use the point cloud template-matching method to refine the road marking candidates via removing the noise clusters with low correlation coefficient. During experiment with a MLS point set of about 2 kilometres in a city center, our method provides a promising solution to the road markings extraction from MLS data.
Highlights
The Advanced Driver Assistance System (ADAS) and selfdriving technology are hot spots of current social development. 10 million self-driving cars will be on the road by 2020 in an in-depth report from BI Intelligence (2016)
With the rapid development of laser scanners and inertial navigation system, Mobile laser scanning (MLS) system is widely used in capturing spatial data of real-world
This paper presents a novel method to automatically extract road markings from MLS point clouds
Summary
The Advanced Driver Assistance System (ADAS) and selfdriving technology are hot spots of current social development. 10 million self-driving cars will be on the road by 2020 in an in-depth report from BI Intelligence (2016). Traditional methods have developed to extract road markings from digital images and videos based on image recognition technology (Ding et al, 2014; WANG et al, 2014). With the rapid development of laser scanners and inertial navigation system, Mobile laser scanning (MLS) system is widely used in capturing spatial data of real-world It is an effective and efficient method for acquiring highly accurate, precise, and dense geo-referencing 3D topographic data (Puente et al, 2013). The reflection intensity is an important information of the laser points, and the road markings’ intensity has a great contrast with surrounding road surface This feature is used to extract road markings in many papers. Segmentation step is performed in the image space by chaining a set of thresholding and morphological filters This method may lose some accuracy when converting point clouds to image.
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