  Exploration of unknown environments is a fundamental problem in robotics and
an essential component in numerous applications of autonomous systems. A major
challenge in exploring unknown environments is that the robot has to plan with
the limited information available at each time step. While most current
approaches rely on heuristics and assumption to plan paths based on these
partial observations, we instead propose a novel way to integrate deep learning
into exploration by leveraging 3D scene completion for informed, safe, and
interpretable exploration mapping and planning. Our approach, SC-Explorer,
combines scene completion using a novel incremental fusion mechanism and a
newly proposed hierarchical multi-layer mapping approach, to guarantee safety
and efficiency of the robot. We further present an informative path planning
method, leveraging the capabilities of our mapping approach and a novel
scene-completion-aware information gain. While our method is generally
applicable, we evaluate it in the use case of a Micro Aerial Vehicle (MAV). We
thoroughly study each component in high-fidelity simulation experiments using
only mobile hardware, and show that our method can speed up coverage of an
environment by 73% compared to the baselines with only minimal reduction in map
accuracy. Even if scene completions are not included in the final map, we show
that they can be used to guide the robot to choose more informative paths,
speeding up the measurement of the scene with the robot's sensors by 35%. We
validate our system on a fully autonomous MAV, showing rapid and reliable scene
coverage even in a complex cluttered environment. We make our methods available
as open-source.
