  Fast, collision-free motion through unknown environments remains a
challenging problem for robotic systems. In these situations, the robot's
ability to reason about its future motion is often severely limited by sensor
field of view (FOV). By contrast, biological systems routinely make decisions
by taking into consideration what might exist beyond their FOV based on prior
experience. In this paper, we present an approach for predicting occupancy map
representations of sensor data for future robot motions using deep neural
networks. We evaluate several deep network architectures, including purely
generative and adversarial models. Testing on both simulated and real
environments we demonstrated performance both qualitatively and quantitatively,
with SSIM similarity measure up to 0.899. We showed that it is possible to make
predictions about occupied space beyond the physical robot's FOV from simulated
training data. In the future, this method will allow robots to navigate through
unknown environments in a faster, safer manner.
