  In this paper, we address the problem of autonomous exploration of unknown
environments with an aerial robot equipped with a sensory set that produces
large point clouds, such as LiDARs. The main goal is to gradually explore an
area while planning paths and calculating information gain in short computation
time, suitable for implementation on an on-board computer. To this end, we
present a planner that randomly samples viewpoints in the environment map. It
relies on a novel and efficient gain calculation based on the Recursive
Shadowcasting algorithm. To determine the Next-Best-View (NBV), our planner
uses a cuboid-based evaluation method that results in an enviably short
computation time. To reduce the overall exploration time, we also use a dead
end resolving strategy that allows us to quickly recover from dead ends in a
challenging environment. Comparative experiments in simulation have shown that
our approach outperforms the current state-of-the-art in terms of computational
efficiency and total exploration time. The video of our approach can be found
at https://www.youtube.com/playlist?list=PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl.
