Data-Based MHE for Agile Quadrotor Flight

TL;DR

This paper introduces a data-driven moving horizon estimation method using Gaussian Processes to improve the accuracy and robustness of state estimation in agile quadrotors during high-speed flights, addressing aerodynamic turbulence challenges.

Contribution

It presents a novel integration of Gaussian Processes into MHE for quadrotors, enhancing state estimation accuracy under turbulent aerodynamic conditions.

Findings

Significant improvement in state estimation accuracy.

Enhanced robustness to poor measurements.

Validated through extensive simulations and experiments.

Abstract

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.