Autonomous Vision-based Landing and Terrain Mapping Using an MPC-controlled Unmanned Rotorcraft

In this paper, we present a vision-based terrain mapping and analysis system, and a model predictive control (MPC)-based flight control system, for autonomous landing of a helicopter-based unmanned aerial vehicle (UAV) in unknown terrain. The vision system is centered around Geyer et al.'s recursive multi-frame planar parallax algorithm (2006), which accurately estimates 3D structure using geo-referenced images from a single camera, as well as a modular and efficient mapping and terrain analysis module. The vision system determines the best trajectory to cover large areas of terrain or to perform closer inspection of potential landing sites, and the flight control system guides the vehicle through the requested flight pattern by tracking the reference trajectory as computed by a real-time MPC-based optimization. This trajectory layer, which uses a constrained system model, provides an abstraction between the vision system and the vehicle. Both vision and flight control results are given from flight tests with an electric UAV.