  We present a Deep Learning based system for the twin tasks of localization
and obstacle avoidance essential to any mobile robot. Our system learns from
conventional geometric SLAM, and outputs, using a single camera, the
topological pose of the camera in an environment, and the depth map of
obstacles around it. We use a CNN to localize in a topological map, and a
conditional VAE to output depth for a camera image, conditional on this
topological location estimation. We demonstrate the effectiveness of our
monocular localization and depth estimation system on simulated and real
datasets.
