Vision-Based Landing of Fixed-Wing Miniature Air Vehicles

Blake Barber
Timothy McLain, Brigham Young University - Provo
Barrett Edwards


This paper outlines a method for using vision-based feedback to accurately land a MAV on a visually identifiable target of approximately known location. The method presented is robust to wind, capable of handling both stationary and moving targets, and capable of correcting for camera misalignment, state estimation biases, and parameter estimation biases. Landing results from actual flight tests are presented that demonstrate the effectiveness of the proposed method.