Fast Terrain Traversability Estimation With Terrestrial Lidar In Off-Road Autonomous Navigation

Autonomous navigation (also known as self-driving) has rapidly advanced in the last decade for on-road vehicles. In contrast, off-road vehicles still lag in autonomous navigation capability. Sensing and perception strategies used successfully in on-road driving fail in the off-road environment. This is because on-road environments can often be neatly categorized both semantically and geometrically into regions like driving lane, road shoulder, and passing lane and into objects like stop sign or vehicle. The off-road environment is neither semantically nor geometrically tidy, leading to not only difficulty in developing perception algorithms that can distinguish between drivable and non-drivable regions, but also difficulty in the determination of what constitutes "drivable" for a given vehicle. In this work, the factors affecting traversability are discussed, and an algorithm for assessing the traversability of off-road terrain in real time is developed and presented. The predicted traversability is compared to ground-truth traversability metrics in simulation. Finally, we show how this traversability metric can be automatically calculated by using physics-based simulation with the MSU Autonomous Vehicle Simulator (MAVS). A simulated off-road autonomous navigation task using a real-time implementation of the traversability metric is presented, highlighting the utility of this approach.