Comparison of Attitude Estimation Techniques for Low-cost Unmanned Aerial Vehicles

Abstract

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.