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In this paper, we address the problem of autonomous exploration of unknownenvironments with an aerial robot equipped with a sensory set that produceslarge point clouds, such as LiDARs. The main goal is to gradually explore anarea while planning paths and calculating information gain in short computationtime, suitable for implementation on an on-board computer. To this end, wepresent a planner that randomly samples viewpoints in the environment map. Itrelies on a novel and efficient gain calculation based on the RecursiveShadowcasting algorithm. To determine the Next-Best-View (NBV), our planneruses a cuboid-based evaluation method that results in an enviably shortcomputation time. To reduce the overall exploration time, we also use a deadend resolving strategy that allows us to quickly recover from dead ends in achallenging environment. Comparative experiments in simulation have shown thatour approach outperforms the current state-of-the-art in terms of computationalefficiency and total exploration time. The video of our approach can be foundat https://www.youtube.com/playlist?list=PLC0C6uwoEQ8ZDhny1VdmFXLeTQOSBibQl.

A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration