Probabilistic Terrain Estimation for Autonomous Off-Road Driving

For autonomous driving in urban environments it is usually assumed that the road is flat. To drive off-road, however, we need a more sophisticated model of the ground surface. While previous work is mapping the terrain along with static obstacles, we propose to separate the tasks and introduce a new approach to probabilistic terrain estimation. It combines recursive Gaussian state estimation with a subsequent maximum a posteriori estimation. This allows us to efficiently accumulate obtained measurements and at the same time get a probabilistic terrain estimate based on a geometric terrain model. This way, also (measurement) uncertainties as well as inter- and extrapolation to unobserved areas are handled stochastically correct. We demonstrate the effectiveness and real-time capability of our approach using real-world data.