  Unmanned Aerial Vehicles have received much attention in recent years due to
its wide range of applications, such as exploration of an unknown environment
to acquire a 3D map without prior knowledge of it. Existing exploration methods
have been largely challenged by computationally heavy probabilistic path
planning. Similarly, kinodynamic constraints or proper sensors considering the
payload for UAVs were not considered. In this paper, to solve those issues and
to consider the limited payload and computational resource of UAVs, we propose
"Peacock Exploration": A lightweight exploration method for UAVs using
precomputed minimum snap trajectories which look like a peacock's tail. Using
the widely known, control efficient minimum snap trajectories and OctoMap, the
UAV equipped with a RGB-D camera can explore unknown 3D environments without
any prior knowledge or human-guidance with only O(logN) computational
complexity. It also adopts the receding horizon approach and simple, heuristic
scoring criteria. The proposed algorithm's performance is demonstrated by
exploring a challenging 3D maze environment and compared with a
state-of-the-art algorithm.
