Terrain Classification from an Aerial Perspective

Terrain knowledge around unmanned ground vehicles (UGVs) is vital for autonomous navigation. Having global understanding of the surroundings of UGVs is important, although the field of view from UGVs is very limited. Thus, we utilize an aerial vehicle to provide a large terrain map from sequential aerial images. In this paper, we present multiple techniques to accelerate the process of terrain classification so that it can run onboard on the aerial platform. The main techniques used to accelerate the process is a "knowledge distillation" of a deep neural net to a shallower one, and a super pixel implementation. We evaluated our system on Jetson TX1 with actual images collected from a weather balloon which confirmed the effectiveness of the proposed system.