  Terrain classification is an important problem for mobile robots operating in
extreme environments as it can aid downstream tasks such as autonomous
navigation and planning. While RGB cameras are widely used for terrain
identification, vision-based methods can suffer due to poor lighting conditions
and occlusions. In this paper, we propose the novel use of Ground Penetrating
Radar (GPR) for terrain characterization for mobile robot platforms. Our
approach leverages machine learning for surface terrain classification from GPR
data. We collect a new dataset consisting of four different terrain types, and
present qualitative and quantitative results. Our results demonstrate that
classification networks can learn terrain categories from GPR signals.
Additionally, we integrate our GPR-based classification approach into a
multimodal semantic mapping framework to demonstrate a practical use case of
GPR for surface terrain classification on mobile robots. Overall, this work
extends the usability of GPR sensors deployed on robots to enable terrain
classification in addition to GPR's existing scientific use cases.
