Comparison of Attitude Estimation Techniques for Low-cost Unmanned Aerial Vehicles
This work addresses attitude estimation challenges for low-cost UAVs, particularly in dynamic conditions, but is incremental as it compares existing methods without introducing new techniques.
The paper compared complementary, extended Kalman, and unscented Kalman filters for attitude estimation in low-cost UAVs, finding that more sophisticated estimators like the unscented Kalman filter offer performance gains in highly dynamic maneuvers, with specific improvements noted in simulation tests.
Attitude estimation for small, low-cost unmanned aerial vehicles is often achieved using a relatively simple complementary filter that combines onboard accelerometers, gyroscopes, and magnetometer sensing. This paper explores the limits of performance of such attitude estimation, with a focus on performance in highly dynamic maneuvers. The complementary filter is derived along with the extended Kalman filter and unscented Kalman filter to evaluate the potential performance gains when using a more sophisticated estimator. Simulations are presented that compare performance across a range of test cases, many where ground truth was generated from manually controlled flights in a flight simulator. Estimator scenarios that are generic across the different estimator types (such as the way sensor information is processed, and the use of dynamically changing gains) are compared across the test cases. An appendix is included as a quick reference for the common attitude representations and their kinematic expressions.