Improving attitude estimation using inertial sensors for quadrotor control systems

Attitude estimation for an aerial vehicle using the Kalman Filter - KF- with experimental validation is presented in this paper. The data fusion is made using simplified representations of the kinematics of the aerial vehicle and the accelerometer measurement model. The resulting algorithm is computationally efficient as it can be run at up to 500 Hz on a low-cost microcontroller. The observer is improved by choosing the appropriate covariance and noise matrices. Numerical and in-flight validation are carried out using an experimental platform and a quadrotor prototype. The experimental results are compared online with the measurements coming from a commercial IMU -Inertial Measurement Unit.