  This paper presents a method for cave surveying in total darkness using 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. Although these dangers can be mitigated by deploying
robots to map dangerous passages and voids, real-time feedback is often needed
to operate robots safely and efficiently. Few state-of-the-art, high-resolution
perceptual modeling techniques attempt to reduce their high bandwidth
requirements to work well with low bandwidth communication channels. 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 approach accommodates both limited field of view depth
cameras and larger field of view LiDAR sensors and is extensively evaluated in
long duration simulations on an embedded PC. An aerial system is leveraged to
demonstrate the repeatability of the approach in a flight arena as well as the
effects of communication dropouts. Finally, the system is deployed in Laurel
Caverns, a commercially owned and operated cave in southwestern Pennsylvania,
USA, and a wild cave in West Virginia, USA.
