This paper presents a method for cave surveying in complete darkness with an autonomous aerial vehicle equipped with a depth camera for mapping, downward-facing camera for state estimation, and forward and downward lights. Traditional methods of cave surveying are labor-intensive and dangerous due to the risk of hypothermia when collecting data over extended periods of time in cold and damp environments, the risk of injury when operating in darkness in rocky or muddy environments, and the potential structural instability of the subterranean environment. Robots could be leveraged to reduce risk to human surveyors, but undeveloped caves are challenging environments in which to operate due to low-bandwidth or nonexistent communications infrastructure. The potential for communications dropouts motivates autonomy in this context. Because the topography of the environment may not be known a priori, it is advantageous for human operators to receive real-time feedback of high-resolution map data that encodes both large and small passageways. Given this capability, directed exploration, where human operators transmit guidance to the autonomous robot to prioritize certain leads over others, lies within the realm of the possible. Few state-of-the-art, high-resolution perceptual modeling techniques quantify the time to transfer the model across low bandwidth, high reliability communications channels such as radio. To bridge this gap in the state of the art, this work compactly represents sensor observations as Gaussian mixture models and maintains a local occupancy grid map for a motion planner that greedily maximizes an information-theoretic objective function. The methodology is extensively evaluated in long duration simulations on an embedded PC and deployed to an aerial system in Laurel Caverns, a commercially owned and operated cave in Southwestern Pennsylvania, USA.

中文翻译：

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使用空中机器人进行自主洞穴测量

本文提出了一种在完全黑暗中进行洞穴勘测的方法，该飞行器配备了用于测绘的深度摄像头、用于状态估计的向下摄像头以及前向和向下照明灯。由于在寒冷和潮湿的环境中长时间收集数据时存在体温过低的风险，在岩石或泥泞环境中在黑暗中操作时存在受伤风险，以及潜在的结构不稳定，传统的洞穴勘测方法是劳动密集型和危险的的地下环境。可以利用机器人来降低人类测量员的风险，但由于低带宽或不存在通信基础设施，未开发的洞穴是具有挑战性的操作环境。在这种情况下，通信中断的可能性激发了自主权。由于环境的地形可能不是先验的，因此人类操作员接收高分辨率地图数据的实时反馈是有利的，这些数据对大通道和小通道进行编码。鉴于这种能力，定向探索，即人类操作员向自主机器人传输指导以将某些线索优先于其他线索，在可能的范围内。很少有最先进的高分辨率感知建模技术可以量化在低带宽、高可靠性通信信道（如无线电）上传输模型的时间。为了弥合现有技术中的这一差距，这项工作将传感器观察紧凑地表示为高斯混合模型，并为贪婪地最大化信息理论目标函数的运动规划器维护了局部占用网格图。