Error-State Kalman Filter Based External Wrench Estimation for MAVs Under a Cascaded Architecture

In many applications such as aerial transportation, delivery, and manipulation, it is essential to know the external wrench exerted on multirotor aerial vehicles precisely. This paper presents an algorithm to estimate external wrench using a rotor speed measurement unit, an inertial measurement unit and a motion capture system. Under a cascaded architecture containing two sub-systems, one error-state Kalman Filter is designed to estimate velocity and attitude and eliminate the bias of the measurement from the inertial measurement unit, the other error-state Kalman Filter is designed to estimate the external wrench. Observability of the two estimation subsystems is verified by the Lie derivative method. The proposed algorithm has been tested in simulations and real-world experiments, which demonstrates its superiority in providing real-time and accurate external wrench estimation.