Autonomous Search for Underground Mine Rescue Using Aerial Robots

In this paper we present a comprehensive solution for autonomous underground mine rescue using aerial robots. In particular, a new class of Micro Aerial Vehicles are equipped with the ability to localize and map in subterranean settings, explore unknown mine environments on their own, and perform detection and localization of objects of interest for the purposes of mine rescue (i.e., "human survivors" and associated objects such as "backpacks", "smartphones" or "tools"). For the purposes of GPS-denied localization and mapping in the visuallydegraded underground environments (e.g., a smoke-filled mine during an accident) the solution relies on the fusion of LiDAR data with thermal vision frames and inertial cues. Autonomous exploration is enabled through a graph-based search algorithm and an online volumetric representation of the environment. Object search is then enabled through a deep learning-based classifier, while the associated location is queried using the online reconstructed map. The complete software framework runs onboard the aerial robots utilizing the integrated embedded processing resources. The overall system is extensively evaluated in real-life deployments in underground mines.