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.
翻译:在本文中,我们用一个空中机器人自主探索未知环境的问题,该机器人装备了一套感官装置,产生大点云,如LiDARs。主要目标是逐步探索一个区域,同时在短计算时间内规划路径和计算信息收益,适合在船上计算机上实施。为此,我们提出一个计划者,在环境地图中随机抽样查看环境图。它依靠基于再侵蚀阴影投影算法的新颖和高效收益计算。为了确定下一个Best-VView(NBV),我们的规划者使用一种基于幼虫的评估方法,在极短的计算时间里得出结果。为了减少总体探索时间,我们还使用一条死路解决战略,使我们能够在具有挑战性的环境中迅速从死胡中恢复过来。模拟比较实验表明,我们的方法在计算效率和总勘探时间方面超过了目前的状况。我们方法的视频可在https://www.youtube.com/playlist?list=PLC6OS-uwEZ8DMVDQQ。