Improved State Estimation in Quadrotor MAVs: A Novel Drift-Free Velocity Estimator
This work addresses state estimation challenges for quadrotor MAVs, offering a domain-specific solution that is incremental in nature.
The paper tackled the problem of state estimation in quadrotor micro aerial vehicles by developing a drift-free velocity estimator, resulting in improved lateral and longitudinal velocity estimates along with enhanced roll and pitch attitude estimations, as validated with real-world data and ground truth from a Vicon system.
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.