  Mobile robots navigating in indoor and outdoor environments must be able to
identify and avoid unsafe terrain. Although a significant amount of work has
been done on the detection of standing obstacles (solid obstructions), not much
work has been done on the detection of negative obstacles (e.g. dropoffs,
ledges, downward stairs). We propose a method of terrain safety segmentation
using deep convolutional networks. Our custom semantic segmentation
architecture uses a single camera as input and creates a freespace map
distinguishing safe terrain and obstacles. We then show how this freespace map
can be used for real-time navigation on an indoor robot. The results show that
our system generalizes well, is suitable for real-time operation, and runs at
around 55 fps on a small indoor robot powered by a low-power embedded GPU.
