Optimizing energy consumption for robot navigation in fields requires
energy-cost maps. However, obtaining such a map is still challenging,
especially for large, uneven terrains. Physics-based energy models work for
uniform, flat surfaces but do not generalize well to these terrains.
Furthermore, slopes make the energy consumption at every location directional
and add to the complexity of data collection and energy prediction. In this
paper, we address these challenges in a data-driven manner. We consider a
function which takes terrain geometry and robot motion direction as input and
outputs expected energy consumption. The function is represented as a
ResNet-based neural network whose parameters are learned from field-collected
data. The prediction accuracy of our method is within 12% of the ground truth
in our test environments that are unseen during training. We compare our method
to a baseline method in the literature: a method using a basic physics-based
model. We demonstrate that our method significantly outperforms it by more than
10% measured by the prediction error. More importantly, our method generalizes
better when applied to test data from new environments with various slope
angles and navigation directions