  This paper develops a data-based moving horizon estimation (MHE) method for
agile quadrotors. Accurate state estimation of the system is paramount for
precise trajectory control for agile quadrotors; however, the high level of
aerodynamic forces experienced by the quadrotors during high-speed flights make
this task extremely challenging. These complex turbulent effects are difficult
to model and the unmodelled dynamics introduce inaccuracies in the state
estimation. In this work, we propose a method to model these aerodynamic
effects using Gaussian Processes which we integrate into the MHE to achieve
efficient and accurate state estimation with minimal computational burden.
Through extensive simulation and experimental studies, this method has
demonstrated significant improvement in state estimation performance displaying
superior robustness to poor state measurements.
