A fuzzy complementary Kalman filter based on visual and IMU data for UAV landing

Accurate and safe landing on a predefined area is very important for UAV (Unmanned Aerial Vehicle) since it is the most prone to accidents during the flight. Attitude estimation using a variety of sensors is an essential task of an UAV landing. In this paper, a novel data fusion algorithm based on a Fuzzy Complementary Kalman filter (FCKF), which combines IMU (Inertial Measurement Unit) data with vision data, is proposed to estimate the attitude of the UAV during autonomous landing. The proposed algorithm operates the error as state variables of the Kalman filter to compensate for the attitude angle obtained from IMU data and vision data, and then uses a complementary filter to estimate the attitude. The fuzzy logic is adopted to adjust the gain of complementary filter to improve the robustness and accuracy of the filter. The good performance of the proposed algorithm is verified by simulations and experiments. The results show that the proposed algorithm has better performance in accuracy and robustness than CKF and EKF.