Attitude Estimation for Normal Flight and Collision Recovery of a Quadrotor UAV

A comparison of attitude estimation algorithms is performed to allow selection of an appropriate algorithm for a quadrotor collision recovery system. A Multiplicative Extended Kalman Filter (MEKF), an Unscented Kalman Filter (UKF), a complementary filter, an H-infinity Filter, and adaptive varieties of the selected filters are chosen for comparison. The adaptive modifications to the estimation algorithms are developed to better estimate the attitude during a collision. The algorithms are compared in simulated normal flight as well as during a simulated collision in order to show which estimation algorithm provides the best quadrotor attitude estimate in all conditions. An approach to modify simulated Inertial Measurement Unit (IMU) data to match experimental data during a quadrotor collision is developed. The results show that slight improvements can be found using the adaptive algorithms and that overall, the UKF algorithms are found to outperform other estimators during regular flight and after a collision.