Vision-only navigation and control of unmanned aerial vehicles using the sigma-point Kalman filter

This paper presents the vision-only navigation and control of a small autonomous helicopter given only measurements from a video camera fixed on the ground. The goal is to develop an alternative to traditional INS/GPS and on-board vision aided systems. The autonomous navigation and control of the helicopter is achieved using a nonlinear state estimator and a state-dependent controller. A key difference to INS/GPS navigation is that measurements of the helicopter's accelerations and angular velocities are not directly available. The state estimation combines the vision measurements with a dynamic model of the vehicle in a recursive filtering procedure using a Sigma-Point Kalman Filter (SPKF). The estimation of the helicopter's current state (position, attitude, velocity, and angular velocity) is then fed back in real-time to a state-dependent Riccati equation (SDRE) controller to generate radio control commands to the helicopter. Simulations are provided comparing performance relative to INS/GPS navigation. Experiments also show that an accurate dynamic model of the vehicle is necessary for closed-loop stability. Our results indicate the feasibility of designing a vision-only estimation and control system capable of stabilizing and maneuvering a small unmanned helicopter. Other than simple on-board avionics for low level actuator control, the ground station is responsible for video capture, state-estimation, and state-feedback flight control.