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The outdoor navigation capabilities of ground robots have improvedsignificantly in recent years, opening up new potential applications in avariety of settings. Cost-based representations of the environment arefrequently used in the path planning domain to obtain an optimized path basedon various objectives, such as traversal time or energy consumption. However,obtaining such cost representations is still cumbersome, particularly inoutdoor settings with diverse terrain types and slope angles. In this paper, weaddress this problem by using a data-driven approach to develop a costrepresentation for various outdoor terrain types that supports two optimizationobjectives, namely energy consumption and traversal time. We train a supervisedmachine learning model whose inputs consists of extracted environment dataalong a path and whose outputs are the predicted energy consumption andtraversal time. The model is based on a ResNet neural network architecture andtrained using field-recorded data. The error of the proposed method ondifferent types of terrain is within 11\% of the ground truth data. To showthat it performs and generalizes better than currently existing approaches onvarious types of terrain, a comparison to a baseline method is made.

Predicting Energy Consumption and Traversal Time of Ground Robots for Outdoor Navigation on Multiple Types of Terrain