"Road modelling using LiDAR-data"
Tove Jungfelt och Elias Sevelin presenterar sitt examensarbete:
The purpose of this master thesis is to locate and find a mathematical model of the road surface from data obtained using LiDAR measurements. 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 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 on accuracy and speed. One of the methods will base its classification mainly on spatial information while the other will tone down this aspect and instead filter results over time. 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 assist in autonomous driving. To reduce complexity in localization and computation we have limited the time scope to less than a second but the overall principles should work over longer time periods. We find that both methods yield good models of the road that reaches between 30 and 40 metres forward. The best result is achieved with time-filtering.