Road Features Extraction Using Terrestrial Mobile Laser Scanning System

In this thesis, we present the experimental research and key contributions we have made in the field of road feature extraction from LiDAR data. We detail the development of three automated algorithms for the extraction of road features from terrestrial mobile LiDAR data. LiDAR data is a rich source of 3D geo-referenced information whose volume and scale have inhibited the development of automated algorithms. Automated feature extraction algorithms enable the wider geospatial industry to transition from traditional road feature surveying approaches to terrestrial mobile laser scanning technologies. Our first contribution to this field is an automated road edge extraction algorithm which can be applied to LiDAR data and navigation information acquired by mobile survey vehicles. This novel algorithm relies on the combination of thresholding and a parametric active contour model to precisely extract road edges. We describe an automated validation algorithm we developed to determine the accuracy of our road edge extraction algorithm. Using the extracted road edges, we are able to accurately extract the road surface from the LiDAR data. This enables us to develop an efficient automated road marking extraction algorithm which is our second contribution. Through the thresholding of the intensity values of road surface LiDAR points, we can extract the road marking LiDAR points. The third contribution of this thesis is the development of an automated road roughness estimation algorithm which is also dependent on the accurate detection of road surface LiDAR points. We fit a surface grid to the LiDAR points representing an ideal road surface and measure the elevation difference between this surface and the actual LiDAR points to compute the surface deviation along a track representing a vehicle wheel. We automated these algorithms through exhaustive examination of optimal parameters and methods for their implementation. To verify these novel algorithms, we tested them on varying types of road sections representing rural, urban and national primary road sections. The research work carried out in the course of this thesis provides valuable insights as well as a prototype road feature extraction tool-set, for both national road authorities and survey companies. These findings and knowledge contribute to a more rapid, cost-effective and comprehensive approach to surveying road networks which, in turn, enables a more efficient, comfortable and safer journey for all road users