  Safe and high-speed navigation is a key enabling capability for real world
deployment of robotic systems. A significant limitation of existing approaches
is the computational bottleneck associated with explicit mapping and the
limited field of view (FOV) of existing sensor technologies. In this paper, we
study algorithmic approaches that allow the robot to predict spaces extending
beyond the sensor horizon for robust planning at high speeds. We accomplish
this using a generative neural network trained from real-world data without
requiring human annotated labels. Further, we extend our existing control
algorithms to support leveraging the predicted spaces to improve collision-free
planning and navigation at high speeds. Our experiments are conducted on a
physical robot based on the MIT race car using an RGBD sensor where were able
to demonstrate improved performance at 4 m/s compared to a controller not
operating on predicted regions of the map.
