  We present VUNet, a novel view(VU) synthesis method for mobile robots in
dynamic environments, and its application to the estimation of future
traversability. Our method predicts future images for given virtual robot
velocity commands using only RGB images at previous and current time steps. The
future images result from applying two types of image changes to the previous
and current images: 1) changes caused by different camera pose, and 2) changes
due to the motion of the dynamic obstacles. We learn to predict these two types
of changes disjointly using two novel network architectures, SNet and DNet. We
combine SNet and DNet to synthesize future images that we pass to our
previously presented method GONet to estimate the traversable areas around the
robot. Our quantitative and qualitative evaluation indicate that our approach
for view synthesis predicts accurate future images in both static and dynamic
environments. We also show that these virtual images can be used to estimate
future traversability correctly. We apply our view synthesis-based
traversability estimation method to two applications for assisted
teleoperation.
