Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator

TL;DR

This paper introduces a novel extended Kalman filter-based state estimator for quadrotors that achieves drift-free velocity and improved attitude estimation by exploiting blade flapping dynamics, validated with real-world data.

Contribution

A new drift-free velocity estimator for quadrotors that incorporates aerodynamic effects within an EKF framework, enhancing state estimation accuracy.

Findings

Drift-free estimation of lateral and longitudinal velocities achieved.

Significant improvements in roll and pitch attitude accuracy.

Validated with real-world quadrotor and Vicon data.

Abstract

This paper describes the synthesis and evaluation of a novel state estimator for a Quadrotor Micro Aerial Vehicle. Dynamic equations which relate acceleration, attitude and the aero-dynamic propeller drag are encapsulated in an extended Kalman filter framework for estimating the velocity and the attitude of the quadrotor. It is demonstrated that exploiting the relationship between the body frame accelerations and velocities, due to blade flapping, enables drift free estimation of lateral and longitudinal components of body frame translational velocity along with improvements to roll and pitch components of body attitude estimations. Real world data sets gathered using a commercial off-the-shelf quadrotor platform, together with ground truth data from a Vicon system, are used to evaluate the effectiveness of the proposed algorithm.