Fused attitude estimation algorithm based on explicit complementary filter and Kalman filter for an indoor quadrotor UAV

This paper proposes a fusion algorithm based on explicit complementary filter (ECF) and Kalman filter using open source autopilot controller to realize flight of indoor quadrotor UAV in the environment of motion capture system (i.e. Optitrack). The fusion algorithm establishes the state equation model of the system based on the attitude angle differential equation. The attitude angle after ECF is used as the observation of the system, and then the extended Kalman filter (EKF) algorithm is used to fuse the measured data of gyroscope, Optitrack, accelerometer and magnetometer. To verify the effectiveness of the algorithm, static and dynamic flight experiments are conducted indoors with the Optitrack environment using a UAV equipped with an IMU. The experimental results show that the fusion algorithm can restrain the attitude angle drift and filter out the noise well when placed in static state, and can track the change of the attitude of the UAV quickly in flight, which improves the attitude angle accuracy of estimation.