Comparison of attitude determination methodologies with low cost inertial measurement unit for autonomous aerial vehicle

Three attitude determination algorithms are compared in this paper. The three methods are the complementary filter, a quaternion-based Kalman filter and a quaternion-based gradient decent algorithm. To investigate and compare their performance, experiments were conducted and the results analysed. This paper shows that the complementary filter requires the least computational power; quaternion based gradient decent algorithm has the best noise filtering ability; quaternion based gradient algorithm has the highest accuracy. Since each algorithm makes use of the quaternion, the quaternion to Euler angle singularity property must be investigated. Experiments conducted show that when Y-rotation approach the singularity position (±90°), the X-rotation drifts away from the reference input. This paper proposes the use of a set of imaginary input which replaces the original input during Y-rotation approaching the singularity position.

[1]  Jan Roskam,et al.  Rigid and elastic airplane flight dynamics and automatic flight control , 1982 .

[2]  F. Aghili,et al.  Driftless 3-D Attitude Determination and Positioning of Mobile Robots By Integration of IMU With Two RTK GPSs , 2013, IEEE/ASME Transactions on Mechatronics.

[3]  Xiaoji Niu,et al.  Civilian Vehicle Navigation: Required Alignment of the Inertial Sensors for Acceptable Navigation Accuracies , 2008, IEEE Transactions on Vehicular Technology.

[4]  Feifei Gao,et al.  Resolving Multidimensional Ambiguity in Blind Channel Estimation of MIMO-FIR Systems via Block Precoding , 2008, IEEE Transactions on Vehicular Technology.

[5]  Mark Euston,et al.  A complementary filter for attitude estimation of a fixed-wing UAV , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Xiaogang Wang,et al.  Adaptive extended Kalman filtering applied to low-cost MEMS IMU/GPS integration for UAV , 2009, 2009 International Conference on Mechatronics and Automation.

[7]  Robert E. Mahony,et al.  Nonlinear Complementary Filters on the Special Orthogonal Group , 2008, IEEE Transactions on Automatic Control.

[8]  J. Kuipers.  Quaternions and Rotation Sequences , 1998 .

[9]  Naser El-Sheimy,et al.  Adaptive Fuzzy Prediction of Low-Cost Inertial-Based Positioning Errors , 2007, IEEE Transactions on Fuzzy Systems.

[10]  Xinhua Wang,et al.  Rapid-convergent nonlinear differentiator , 2011, ArXiv.

[11]  Michael V. Cook,et al.  Flight Dynamics Principles: A Linear Systems Approach to Aircraft Stability and Control , 2007 .

[12]  Bijan Shirinzadeh,et al.  Comparison of system parameter identification techniques for a small UAV , 2011, ICM 2011.

[13]  Bijan Shirinzadeh.  Laser‐interferometry‐based tracking for dynamic measurements , 1998 .

[14]  Andrey Soloviev,et al.  Tight Coupling of GPS and INS for Urban Navigation , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Bijan Shirinzadeh,et al.  Dual-position sensitive diode-based orientation measurement in laser-interferometry-based sensing and measurement technique , 2001, Optics East.

[16]  Kai-Yuan Cai,et al.  Quadrotor aircraft control without velocity measurements , 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

[17]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[18]  D. Gebre-Egziabher,et al.  A gyro-free quaternion-based attitude determination system suitable for implementation using low cost sensors , 2000, IEEE 2000. Position Location and Navigation Symposium (Cat. No.00CH37062).