Performance analysis of a Kalman Filter based attitude estimator for a Quad Rotor UAV

Abstract

This paper analyzes the performance of an Extended Kalman Filter (EKF) based attitude estimator for a Quad Rotor Unmanned Aerial Vehicle (QRUAV). A non-linear dynamic model of the QRUAV, simulated in Matlab was complemented with an EKF based attitude estimator. The estimator fuses measurements obtained from simulated accelerometers and gyroscopes onboard the QRUAV. It is then shown that this estimator only results in adequate representation of the attitude when the translational velocity is large. Thisbehavior is explained considering the dynamics of the vehicle and the onboard accelerometers. Also a possible method of exploiting this behavior for improving the estimation accuracy is suggested.

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

“…7 shows the attitude estimates of the proposed EKF together with the ground truth obtained from the Vicon system. For comparison purposes, we have also plotted the attitude estimates from a generic estimator as detailed in [11] in Fig. 9.…”

“…Attitude estimators for MAVs overcome this issue by assuming that accelerometers predominantly measure gravitational acceleration and are thus capable of providing low frequency information about MAV orientation with respect to gravity. Clearly, when the vehicle accelerations are significant, as in the case of quadrotor, this assumption does not hold [11]. Furthermore, such estimators are incapable of drift free velocity estimation, as they can only be generated by integrating noisy accelerometer measurements.…”

Visual Collaboration Leader-Follower UAV-Formation for Indoor Exploration

“…High frequency measurements from the inherent Inertial Measurement Unit (IMU) within each UAV are filtered using an Extended-Kalman-Filter (EKF) for attitude estimation ( Abeywardena and Munasinghe, 2010 ) in Flight Control Units (FCUs). Migrating from the attitude to the altitude estimation necessitates the use of additional onboard sensors.…”

An Improved Model-Based Observer for Inertial Navigation for Quadrotors with Low Cost IMUs

“…Recently, several researchers have proposed using the dynamics of multirotor helicopters to replace the integrated outputs of a portion of the inertial navigation system with an observer. [17][18][19][20][21] In typical integrated INS methods, errors propagate due to alignment errors (i.e. modeling the IMU in an incorrect frame of reference), sensor errors (i.e.…”

“…In particular, the accelerometers measure the specific force produced by the thrust of the rotors and this was used to obtain improved pitch and roll measurements. 17 Researchers then proposed that accelerometers in the plane of the propellers actually measure force due to the so called H-force drag. A simplified model of this force is proportional to the velocity of the vehicle in the plane of the rotors.…”