Evaluation of data fusion algorithms for attitude estimation of unmanned aerial vehicles
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ISSN: 1680-8894
E-ISSN: 2219-6714
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Nov 21, 2017
Published: Nov 21, 2017
Published: Nov 21, 2017
Abstract
The aim of this study was to evaluate and compare the three most commonly used data processing algorithms for Attitude and Heading Reference Systems (AHRS) for unmanned aerial vehicles (UAVs), which implement filtering processes and data fusion. These algorithms are the Kalman filter, Madgwick algorithm and Mahony algorithm. Commercially, there are several types of IMU / Magnetometer sensors, which provide a very good feedback of an aircraft states. However, they tend to be very expensive, so in this paper we focus on those who have a medium cost and a good cost / performance ratio for use with UAVs. A methodology was developed so we could compare what algorithm adapts better to systems with different characteristics. The results showed that the Mahony algorithm worked better due to its faster convergence. Of the three angles of rotation around the main axes xyz, the angle around z (ψ) showed the largest error, which indicates that there is still some deficiency from those estimates which depend on the magnetometer.
Keywords
AHRS, data fusion algorithms, Madgwick algorithm, Mahony algorithm, UAVs, Kalman filterDownloads
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Chérigo, C., & Rodríguez, H. (2017). Evaluation of data fusion algorithms for attitude estimation of unmanned aerial vehicles. I+D Tecnológico, 13(2), 90-99. Retrieved from https://revistas.utp.ac.pa/index.php/id-tecnologico/article/view/1719
References
(1) H. B. Mitchell, “Multi-Sensor Data Fusion An introduction”, Berlin: New York, Springer Verlag, p. 3, 2007.
(2) Damien Douxchamps, “A small list of IMU/INS/INU” [en línea], http://damien.douxchamps.net/research/imu/, 2016.
(3) R. Munguía and A. Grau, “A Practical Method for Implementing an Attitude and Heading Reference System”, Department of computer Science, CUCEI, Universidad de Guadalajar, Mexico, International journal of advanced Robotic Systems, p. 12, 2014.
(4) J. A. Camarena, “El Filtro de Kalman”[en línea], http://dep.fie.umich.mx/~camarena/FiltroKalman.pdf, 2014.
(5) R. Faragher, "Understanding the basis of the kalman filter via a simple and intuitive derivation",IEEE Signal Process. Mag., vol. 29, no. 5, pp. 128-132, 2012
(6) B. Barshan and H. F. Durrant-Whyte. Inertial navigation systems for mobile robots.11(3):328-342, June 1995.
(7) E. Foxlin, "Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter", Proceedings of VRAIS '96, pp. 185-194
(8) J. L. Marins, Xiaoping Yun, E. R. Bachmann, R. B. McGhee and M. J. Zyda, "An extended kalman filter for quaternion- based orientation estimation using marg sensors", Proceedings of the IEEE/RSJ International Conference on intelligent Robots and Systems, vol. 4, pp. 2003-2011
(9) Xsens Technologies B.V. MTi and MTx, “User Manual and Technical Documentation.Pantheon 6a”, 7521 PR Enschede, The Netherlands, 2009
(10) MicroStrain Inc. 3DM-GX3 -25 Miniature Attutude Heading Reference Sensor. 459 Hurricane Lane, Suite 102, Williston, VT 05495 USA, 1.04 edition, 2009.
(11) Crossbow Technology, Inc. AHRS400 Series Users Manual. 4145 N. First Street, San Jose, CA 95134, rev. c edition, February 2007.
(12) S. O. H. Madgwick, A. J. L. Harrison and R. Vaidyanathan, "Estimation of IMU and MARG orientation using a gradient descent algorithm,"Proc. IEEE Int. Conf. Rehabil. Robot., pp. 1-7
(13) R. Mahony, T. Hamel and J-M. Pflimlin, “Complimentary filter design on the special orthogonal group SO(3)”, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronic Engineers, Seville, Spain, 2005.
(14) M. Leccadito, “An Attitude Heading Reference System using a Low Cost Inertial Measurement Unit”, Virginia Commonwealth University, Richmond, Virginia, 2013.
(15) B. McCarron, “Low-Cost Implementation via Sensor Fusion Algorithms in the Arduino Environment”, California Polytechnic State University, San Luis Obispo, p. 17, 2013.
(16) M. Euston, Paul Coote, Robert Mahony, Jhonghyuk Kim and Tarek Hamel, “A complementary Filter for Attitude Estimation of a Fixed-Wing UAV”,RAL Robotic and Autonomy Lab Australian National University, p. 6, 2008.
(17) A. Cavallo, A. Cirillo, P. Cirillo, G. De Maria, P. Falco, C. Natale and S. Pirozzi, “Experimental Comparison of Sensors Fusion Algorithms for Attitude Estimation”, The international Federation of Automatic Control cape Town, South Africa. 19th world congress, p. 6, 2014.
(2) Damien Douxchamps, “A small list of IMU/INS/INU” [en línea], http://damien.douxchamps.net/research/imu/, 2016.
(3) R. Munguía and A. Grau, “A Practical Method for Implementing an Attitude and Heading Reference System”, Department of computer Science, CUCEI, Universidad de Guadalajar, Mexico, International journal of advanced Robotic Systems, p. 12, 2014.
(4) J. A. Camarena, “El Filtro de Kalman”[en línea], http://dep.fie.umich.mx/~camarena/FiltroKalman.pdf, 2014.
(5) R. Faragher, "Understanding the basis of the kalman filter via a simple and intuitive derivation",IEEE Signal Process. Mag., vol. 29, no. 5, pp. 128-132, 2012
(6) B. Barshan and H. F. Durrant-Whyte. Inertial navigation systems for mobile robots.11(3):328-342, June 1995.
(7) E. Foxlin, "Inertial Head-Tracker Sensor Fusion by a Complementary Separate-Bias Kalman Filter", Proceedings of VRAIS '96, pp. 185-194
(8) J. L. Marins, Xiaoping Yun, E. R. Bachmann, R. B. McGhee and M. J. Zyda, "An extended kalman filter for quaternion- based orientation estimation using marg sensors", Proceedings of the IEEE/RSJ International Conference on intelligent Robots and Systems, vol. 4, pp. 2003-2011
(9) Xsens Technologies B.V. MTi and MTx, “User Manual and Technical Documentation.Pantheon 6a”, 7521 PR Enschede, The Netherlands, 2009
(10) MicroStrain Inc. 3DM-GX3 -25 Miniature Attutude Heading Reference Sensor. 459 Hurricane Lane, Suite 102, Williston, VT 05495 USA, 1.04 edition, 2009.
(11) Crossbow Technology, Inc. AHRS400 Series Users Manual. 4145 N. First Street, San Jose, CA 95134, rev. c edition, February 2007.
(12) S. O. H. Madgwick, A. J. L. Harrison and R. Vaidyanathan, "Estimation of IMU and MARG orientation using a gradient descent algorithm,"Proc. IEEE Int. Conf. Rehabil. Robot., pp. 1-7
(13) R. Mahony, T. Hamel and J-M. Pflimlin, “Complimentary filter design on the special orthogonal group SO(3)”, Proceedings of the IEEE Conference on Decision and Control, Institute of Electrical and Electronic Engineers, Seville, Spain, 2005.
(14) M. Leccadito, “An Attitude Heading Reference System using a Low Cost Inertial Measurement Unit”, Virginia Commonwealth University, Richmond, Virginia, 2013.
(15) B. McCarron, “Low-Cost Implementation via Sensor Fusion Algorithms in the Arduino Environment”, California Polytechnic State University, San Luis Obispo, p. 17, 2013.
(16) M. Euston, Paul Coote, Robert Mahony, Jhonghyuk Kim and Tarek Hamel, “A complementary Filter for Attitude Estimation of a Fixed-Wing UAV”,RAL Robotic and Autonomy Lab Australian National University, p. 6, 2008.
(17) A. Cavallo, A. Cirillo, P. Cirillo, G. De Maria, P. Falco, C. Natale and S. Pirozzi, “Experimental Comparison of Sensors Fusion Algorithms for Attitude Estimation”, The international Federation of Automatic Control cape Town, South Africa. 19th world congress, p. 6, 2014.