Road modelling using LiDAR-data

Abstract: The purpose of our master thesis is to locate and find a mathematical model of the road surface from data obtained using LiDAR measure- ments. A LiDAR is an optical instrument that generates a point cloud of its surroundings by measuring distance and intensity. Some LiDARs also generate additional metrics, but for our road model only the distance will be used. However, we will discuss how the intensity can be used when extracting road marking lines. The data used is from the KITTI Vision Benchmark Suite [8] and was generated using a Velodyne HDL-64 LiDAR. We have also simulated how our algorithm performs with lower resolution LiDARs. We will present two different methods and evaluate their performance with regards to accuracy and speed. The methods will divide the data generated from the LiDAR into triangular sections, and classify each sec- tion as either road or not road. One of the methods will base its clas- sification mainly on spatial information while the other will tone down this aspect and instead filter results over time. To reduce complexity in localization and computation, we have limited the filtering to a duration of less than a second but the overall principles should work over longer time periods. Due to a lack of pre-existing models of the road, we will only be able to evaluate our methods visually. A goal has been that the methods, with some alterations, should be able to run in real-time to as- sist in autonomous driving. We find that both methods yield good models of the road that reaches between 30 and 40 metres forward. The method using time filtering shows the most promise, but the method with focus on spatial dependency behaves better in some scenarios. Therefore, we suggest combining the two methods in future work.