  The outdoor navigation capabilities of ground robots have improved
significantly in recent years, opening up new potential applications in a
variety of settings. Cost-based representations of the environment are
frequently used in the path planning domain to obtain an optimized path based
on various objectives, such as traversal time or energy consumption. However,
obtaining such cost representations is still cumbersome, particularly in
outdoor settings with diverse terrain types and slope angles. In this paper, we
address this problem by using a data-driven approach to develop a cost
representation for various outdoor terrain types that supports two optimization
objectives, namely energy consumption and traversal time. We train a supervised
machine learning model whose inputs consists of extracted environment data
along a path and whose outputs are the predicted energy consumption and
traversal time. The model is based on a ResNet neural network architecture and
trained using field-recorded data. The error of the proposed method on
different types of terrain is within 11\% of the ground truth data. To show
that it performs and generalizes better than currently existing approaches on
various types of terrain, a comparison to a baseline method is made.
