  Autonomous navigation in unstructured natural environments poses a
significant challenge. In goal navigation tasks without prior information, the
limited look-ahead of onboard sensors utilised by robots compromises path
efficiency. We propose a novel approach that leverages an above-the-canopy
aerial map for improved ground robot navigation. Our system utilises aerial
lidar scans to create a 3D probabilistic occupancy map, uniquely incorporating
the uncertainty in the aerial vehicle's trajectory for improved accuracy. Novel
path planning cost functions are introduced, combining path length with
obstruction risk estimated from the probabilistic map. The D-Star Lite
algorithm then calculates an optimal (minimum-cost) path to the goal. This
system also allows for dynamic replanning upon encountering unforeseen
obstacles on the ground. Extensive experiments and ablation studies in
simulated and real forests demonstrate the effectiveness of our system.
