  Autonomous exploration is a widely studied fundamental application in the
field of quadrotors, which requires them to automatically explore unknown space
to obtain complete information about the environment. The frontier-based
method, which is one of the representative works on autonomous exploration,
drives autonomous determination by the definition of frontier information, so
that complete information about the environment is available to the quadrotor.
However, existing frontier-based methods are able to accomplish the task but
still suffer from inefficient exploration. How to improve the efficiency of
autonomous exploration is the focus of current research. Typical problems
include slow frontier generation, which affects real-time viewpoint
determination, and insufficient determination methods that affect the quality
of viewpoints. Therefore, to overcome these problems, this paper proposes a
two-level viewpoint determination method for frontier-based autonomous
exploration. Firstly, a sampling-based frontier detection method is presented
for faster frontier generation, which improves the immediacy of environmental
representation compared to traditional traversal-based methods. Secondly, we
consider the access to environmental information during flight for the first
time and design an innovative heuristic evaluation function to decide on a
high-quality viewpoint as the next local navigation target in each exploration
iteration. We conducted extensive benchmark and real-world tests to validate
our method. The results confirm that our method optimizes the frontier search
time by 85%, the exploration time by around 20-30%, and the exploration path by
25-35%.
