Extended Kalman Filter on SE(3) for Geometric Control of a Quadrotor UAV

TL;DR

This paper introduces a novel extended Kalman filter on SE(3) for quadrotor UAVs, enabling accurate state estimation in GPS-denied environments and during sensor failures, with demonstrated numerical and experimental validation.

Contribution

It develops the first coordinate-free EKF on SE(3) that accounts for coupling effects in rotational and translational dynamics for quadrotor control.

Findings

Effective state estimation in GPS-denied scenarios

Robust performance during sensor failures

Successful numerical and experimental validation

Abstract

An extended Kalman filter (EKF) is developed on the special Euclidean group, SE(3) for geometric control of a quadrotor UAV. It is obtained by performing an extensive linearization on SE(3) to estimate the state of the quadrotor from noisy measurements. Proposed estimator considers all the coupling effects between rotational and translational dynamics, and it is developed in a coordinate-free fashion. The desirable features of the proposed EKF are illustrated by numerical examples and experimental results for several scenarios. The proposed estimation scheme on SE(3) has been unprecedented and these results can be particularly useful for aggressive maneuvers in GPS denied environments or in situations where parts of onboard sensors fail.