  In this paper, we address the challenge of exploring unknown indoor aerial
environments using autonomous aerial robots with Size Weight and Power (SWaP)
constraints. The SWaP constraints induce limits on mission time requiring
efficiency in exploration. We present a novel exploration framework that uses
Deep Learning (DL) to predict the most likely indoor map given the previous
observations, and Deep Reinforcement Learning (DRL) for exploration, designed
to run on modern SWaP constraints neural processors. The DL-based map predictor
provides a prediction of the occupancy of the unseen environment while the
DRL-based planner determines the best navigation goals that can be safely
reached to provide the most information. The two modules are tightly coupled
and run onboard allowing the vehicle to safely map an unknown environment.
Extensive experimental and simulation results show that our approach surpasses
state-of-the-art methods by 50-60% in efficiency, which we measure by the
fraction of the explored space as a function of the length of the trajectory
traveled.
