  Autonomous exploration is a fundamental problem for various applications of
unmanned aerial vehicles. Existing methods, however, were demonstrated to
insufficient exploration rate, due to the lack of efficient global coverage,
conservative motion plans and low decision frequencies. In this paper, we
propose FUEL, a hierarchical framework that can support Fast UAV Exploration in
complex unknown environments. We maintain crucial information in the entire
space required by exploration planning by a frontier information structure
(FIS), which can be updated incrementally when the space is explored. Supported
by the FIS, a hierarchical planner plans exploration motions in three steps,
which find efficient global coverage paths, refine a local set of viewpoints
and generate minimum-time trajectories in sequence. We present extensive
benchmark and real-world tests, in which our method completes the exploration
tasks with unprecedented efficiency (3-8 times faster) compared to
state-of-the-art approaches. Our method will be made open source to benefit the
community.
