Aerial-Ground Collaborative Continuous Risk Mapping for Autonomous Driving of Unmanned Ground Vehicle in Off-Road Environments

Terrain risk estimation has always been a crucial but challenging task for autonomous driving of unmanned ground vehicles (UGV) in off-road environments. The map with dense and accurate risk information can help UGV avoid special obstacles such as steep slopes, pits, and ditches while path planning. To solve this problem, this article proposes an aerial-ground collaboration system for continuous risk mapping in off-road environments, utilizing the flexibility and perspective advantages of unmanned aerial vehicles (UAVs). In the system, UAV carries a downward 2-D laser and a visual inertial system to construct a bird's eye map, while UGV is equipped with a 3-D LiDAR for local mapping. In detail, our system focuses on two aspects, which are cross-view map matching and collaborative continuous risk mapping. For cross-view map matching, risk values for point clouds from aerial and ground platforms are calculated and used for terrain segmentation. Then, point-to-voxel residual is built for terrain point sets to accelerate matching. For collaborative continuous risk mapping, an entropy based probabilistic fusion is proposed for accurate risk fusion of overlapping cells. Then, a Bayesian generalized kernel inference algorithm is adapted to predict risk values of the remaining unknown areas. Simulation and real-world experimental results show that the dense and continuous global risk map constructed by the proposed system can effectively assist UGV for path planning in off-road environments.