  Planning a path for a mobile robot typically requires building a map (e.g.,
an occupancy grid) of the environment as the robot moves around. While
navigating in an unknown environment, the map built by the robot online may
have many as-yet-unknown regions. A conservative planner may avoid such regions
taking a longer time to navigate to the goal. Instead, if a robot is able to
correctly predict the occupancy in the occluded regions, the robot may navigate
efficiently. We present a self-supervised occupancy prediction technique,
ProxMaP, to predict the occupancy within the proximity of the robot to enable
faster navigation. We show that ProxMaP generalizes well across realistic and
real domains, and improves the robot navigation efficiency in simulation by
12.40% against a traditional navigation method. We share our findings and code
at https://raaslab.org/projects/ProxMaP.
