Autonomous landing of small unmanned aerial rotorcraft based on monocular vision in GPS-denied area

Focusing on the low-precision attitude of a current small unmanned aerial rotorcraft at the landing stage, the present paper proposes a new attitude control method for the GPS-denied scenario based on the monocular vision. Primarily, a robust landmark detection technique is developed which leverages the well-documented merits of supporting vector machines (SVMs) to enable landmark detection. Then an algorithm of nonlinear optimization based on Newton iteration method for the attitude and position of camera is put forward to reduce the projection error and get an optimized solution. By introducing the wavelet analysis into the adaptive Kalman filter, the high frequency noise of vision is filtered out successfully. At last, automatic landing tests are performed to verify the method´s feasibility and effectiveness.