Train of Autonomous Aerial Vehicles for Subterranean Exploration with SLAM Capabilities

This work realizes a system of drones for the exploration of caves and lava tubes. The presented system consists of lightweight quadcopters that employ Visual-Inertial Odometry for real-time localization and Time of Flight occupancy grid mapping for real-time 3D mapping, yielding similar results to the online SLAM solution but without the need for a 360 degree 3D LiDAR scanner. A waypoint-based Leader-Follower algorithm and a custom MAVLink-based ground station are deployed to control the system and direct the swarm of UAVs in a train configuration. Software-in-the-Loop simulations and complete system flight tests in an artificial cave environment area conducted to evaluate system communication, navigation, and 3D mapping ability. Robust leader-follower configuration, 3D occupancy mapping at resolutions of 0.05 meter, and accurate localization in GPS-denied and low-light conditions are all demonstrated.