  Exploring an unknown environment without colliding with obstacles is one of
the essentials of autonomous vehicles to perform diverse missions such as
structural inspections, rescues, deliveries, and so forth. Therefore, unmanned
aerial vehicles (UAVs), which are fast, agile, and have high degrees of
freedom, have been widely used. However, previous approaches have two
limitations: a) First, they may not be appropriate for exploring large-scale
environments because they mainly depend on random sampling-based path planning
that causes unnecessary movements. b) Second, they assume the pose estimation
is accurate enough, which is the most critical factor in obtaining an accurate
map. In this paper, to explore and map unknown large-scale environments rapidly
and accurately, we propose a novel exploration method that combines the
pre-calculated Peacock Trajectory with graph-based global exploration and
active loop-closing. Because the two-step trajectory that considers the
kinodynamics of UAVs is used, obstacle avoidance is guaranteed in the
receding-horizon manner. In addition, local exploration that considers the
frontier and global exploration based on the graph maximizes the speed of
exploration by minimizing unnecessary revisiting. In addition, by actively
closing the loop based on the likelihood, pose estimation performance is
improved. The proposed method's performance is verified by exploring 3D
simulation environments in comparison with the state-of-the-art methods.
Finally, the proposed approach is validated in a real-world experiment.
