Research Papers

Hybrid Low-Cost Approach for Quadrotor Attitude Estimation

[+] Author and Article Information
Bara J. Emran, Muhannad Al-Omari, Mamoun F. Abdel-Hafez

Mechanical Engineering Department,
American University of Sharjah,
Sharjah 26666, UAE

Mohammad A. Jaradat

Mechanical Engineering Department,
American University of Sharjah,
Sharjah 26666, UAE
Department of Mechanical Engineering,
Jordan University of Science and Technology,
Irbid 22110, Jordan

Manuscript received December 31, 2013; final manuscript received September 7, 2014; published online February 11, 2015. Assoc. Editor: Dr. Corina Sandu.

J. Comput. Nonlinear Dynam 10(3), 031010 (May 01, 2015) (9 pages) Paper No: CND-13-1332; doi: 10.1115/1.4028524 History: Received December 31, 2013; Revised September 07, 2014; Online February 11, 2015

This paper presents a new approach to estimate the orientation of a quadrotor using single low-cost inertial measurement unit (IMU) sensor. The proposed hybrid solution uses two extended Kalman filters (EKF) along with a direction cosine matrix (DCM) algorithm. An EKF utilizes the dynamics of the quadrotor to filter the noise on the body-frame (B-frame) angular rates measured by the three-axis gyroscope sensor. Then, a DCM algorithm uses the filtered gyro signal along with the reading from a three-axis accelerometer and a three-axis magnetometer sensor to estimate the Euler angles. Finally, an additional EKF is presented to enhance the final estimates of the Euler angles. The performance of the proposed hybrid approach is tested and compared with other commonly used methods. Results are presented at the end of the paper to verify the performance of the proposed method. The results show an improvement in the estimated quadrotor's state. Monte Carlo tests are performed to ensure sustained high accuracy estimation.

Copyright © 2015 by ASME
Your Session has timed out. Please sign back in to continue.


Mahony, R., Kumar, V., and Corke, P., 2012, “Multirotor Aerial Vehicles Modeling, Estimation, and Control of Quadrotor,” IEEE Rob. Autom. Mag., 19(3), pp. 20–32. [CrossRef]
Sa, I., and Corke, P., 2012, “System Identification Estimation and Control for a Cost Effective Open-Source Quadcopter,” IEEE International Conference on Robotics and Automation, Saint Paul, MN, May 14–18, pp. 2202–2209.
Al-Omari, M. A. R., Jaradat, M. A., and Jarrah, M. A., 2013, “Integrated Simulation Platform for Indoor Quadrotor Applications,” Proceedings of the 9th International Symposium on Mechatronics and its Applications, Amman, Jordan, April 9–11, pp. 1–6. [CrossRef]
Bottasso, C. L., Luraghi, F., Maffezzoli, A., and Maisano, G., 2010, “Parameter Estimation of Multibody Models of Unstable Systems From Experimental Data, With Application to Rotorcraft Vehicles,” ASME J. Comput. Nonlinear Dyn, 5(3), pp. 88–97. [CrossRef]
Jiménez, A. R., Seco, F., Prieto, C., and Guevara, J., 2009, “A Comparison of Pedestrian Dead-Reckoning Algorithms Using a Low-Cost MEMS IMU,” IEEE International Symposium on Intelligent Signal Processing, Aug. 26–28, Budapest, Hungary, pp. 37–42. [CrossRef]
Abdel-Hafez, M. F., 2010, “The Autocovariance Least Squares Technique for GPS Measurement Noise Estimation,” IEEE Trans. Veh. Technol., 59(2), pp. 574–588. [CrossRef]
Wu, F.-M., Yang, Y.-X., and Zhang, L.-P., 2012, “A New Fusion Scheme for Accuracy Enhancement and Error Modification in GPS/INS Tight Integrated Navigation,” Surv. Rev., 44(326), pp. 208–214. [CrossRef]
Abdel-Hafez, M. F., 2014, “Detection of Bias in GPS Satellites' Measurements: A Probability Ratio Test Formulation,” IEEE Trans. Control Syst. Technol., 22(3), pp. 1166–1173. [CrossRef]
Jaradat, M. A., and Abdel-Hafez, M. F., 2014, “Enhanced, Delay Dependent, Intelligent Fusion for INS/GPS Navigation System,” IEEE Sens. J., 14(5), pp. 1545–1554. [CrossRef]
Hana, S., and Wang, J., 2010, “Land Vehicle Navigation With the Integration of GPS and Reduced INS: Performance Improvement With Velocity Aiding,” J. Navigation, 63(1), pp. 153–166. [CrossRef]
Mahony, R., Hamel, T., and Pflimlin, J.-M., 2008, “Nonlinear Complementary Filters on the Special Orthogonal Group,” IEEE Trans. Autom. Control, 53(5), pp. 1203–1218. [CrossRef]
Euston, M., Coote, P., Mahony, R., Kim, J., and Hamel, T., 2008, “A Complementary Filter for Attitude Estimation of a Fixed-Wing UAV,” International Conference on Intelligent Robots and Systems, Nice, France, Sept. 22–26, pp. 340–345.
Baldwin, G., Mahony, R., Trumpf, J., Hamel, T., and Cheviron, T., 2007, “Complementary Filter Design on the Special Euclidean Group SE (3),” European Control Conference (ECC 2007), Kos, Greece, July, 2007, pp. 3763–3770.
Mahony, R., Cha, S. H., and Hamel, T., 2006, “A Coupled Estimation and Control Analysis for Attitude Stabilization of Mini Aerial Vehicles,” Australiasian Conference on Robotics and Automation, Auckland, New Zealand, November 2006, pp. 1–10.
Stowers, J., Bainbridge-Smith, A., Hayes, M., and Mills, S., 2009, “Optical Flow for Heading Estimation of a Quadrotor Helicopter,” Int. J. Micro Air Veh., 1(4), pp. 229–239. [CrossRef]
Grabe, V., Bulthoff, H. H., and Giordano, P. R., 2012, “On-Board Velocity Estimation and Closed-Loop Control of a Quadrotor UAV Based on Optical Flow,” IEEE International Conference on Robotics and Automation, Saint Paul, MN, May 14–18, pp. 491–497.
Wunschel, D., Lange, S., and Protzel, P., 2012, “Motion Estimation for Autonomous Quadrocopter Indoor Flight,” International Multi-Conference on Systems, Signals and Devices, Chemnitz, Germany, March pp. 1–6.
Natraj, A., Ly, D., Eynard, D., Demonceaux, C., and Vasseur, P., 2013, “Omnidirectional Vision for UAV: Applications to Attitude, Motion and Altitude Estimation for Day and Night Conditions,” J. Intell. Rob. Syst., 69(1–4), pp. 459–473. [CrossRef]
Mondragon, I., Olivares-Mendez, M., Campoy, P., Martinez, C., and Mejias, L., 2010, “Unmanned Aerial Vehicles UAVs Attitude, Height, Motion Estimation and Control Using Visual Systems,” Auton. Rob., 29(1), pp. 17–34. [CrossRef]
Zhang, T., Li, W., Achtelik, M., Kuhnlenz, K., and Buss, M., 2009, “Multi-Sensory Motion Estimation and Control of a Mini-Quadrotor in an Air-Ground Multi-Robot System,” IEEE International Conference on Robotics and Biomimetics (ROBIO), Guilin, China, Dec. 9–23, pp. 45–50.
Lee, G. H., Achtelik, M., Fraundorfer, F., Pollefeys, M., and Siegwart, R., 2010 “A Benchmarking Tool for MAV Visual Pose Estimation,” 11th International Conference on Control Automation Robotics and Vision, Singapore, Dec. 7–10, pp. 1541–1546.
Bošnak, M., Matko, D., and Blažič, S., 2012, “Quadrocopter Control Using an on-Board Video System With Off-Board Processing,” Rob. Auton. Syst., 60(4), pp. 657–667. [CrossRef]
Kis, L., and Lantos, B., 2012, “Time-Delay Extended State Estimation and Control of a Quadrotor Helicopter,” 20th Mediterranean Conference on Control and Automation (MED), Barcelona, Spain, July 3–6, pp. 1560–1565.
Zhang, T., Li, W., Kuhnlenz, K., and Buss, M., 2011, “Multi-Sensory Motion Estimation and Control of an Autonomous Quadrotor,” Adv. Rob., 25(11–12), pp. 1493–1514. [CrossRef]
Hoffmann, F., Goddemeier, N., and Bertram, T., 2010, “Attitude Estimation and Control of a Quadrocopter,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, Oct. 18–22, pp. 1072–1077.
Lendek, Z., Berna, A., Guzman-Gimenez, J., Sala, A., and Garcia, P., 2011, “Application of Takagi–Sugeno Observers for State Estimation in a Quadrotor,” IEEE Conference on Decision and Control and European Control Conference, Orlando, FL, Dec. 12–15, pp. 7530–7535.
Edwan, E., Zhang, J., Zhou, J., and Loffeld, O., 2011, “Reduced DCM Based Attitude Estimation Using Low-Cost IMU and Magnetometer Triad,” 8th Workshop on Positioning Navigation and Communication (WPNC), Dresden, Germany, Apr. 7,8, pp. 1–6.
Nguyen, H. Q. P., Kang, H.-J., Suh, Y.-S., and Ro, Y.-S., 2009, INS/GPS Integration System With DCM Based Orientation Measurement, Emerging Intelligent Computing Technology and Applications (Lecture Notes in Computer Science), Springer, Berlin, Germany.
Ercan, Z., Sezel, V., Heceoglu, H., Dikilitas, C., Gokasan, M., Mugan, A., and Bogosyan, S., 2011, “Multi-Sensor Data Fusion of DCM Based Orientation Estimation for Land Vehicles,” Proceedings of the 2011 IEEE International Conference on Mechatronics, Istanbul, Turkey, Apr. 13–15, pp. 672–677.
Phuong, N. H. Q., Kang, H. J., Suh, Y. S., and Ro, Y. S., 2009, “A DCM Based Orientation Estimation Algorithm With an Inertial Measurement Unit and a Magnetic Compass,” J. Univers. Comput. Sci., 15(4), pp. 859–876. [CrossRef]
Bresciani, T., 2008, “Modelling, Identification and Control of a Quadrotor Helicopter,” Master Thesis, Department of Automatic Control, Lund University, Lund, Sweden.
Bouabdallah, S., Murrieri, P., and Siegwart, R., 2004, “Design and Control of an Indoor Micro Quadrotor,” International Conference on Robotics and Automation, New Orleans, LA, Apr. 26–May 1, pp. 4393–4398.
Johnson, E., and Turbe, M., 2006, “Modeling, Control, and Flight Testing of a Small Ducted Fan Aircraft,” J. Guid. Control Dyn., 29(4), pp. 769–779. [CrossRef]
Premerlani, W., and Bizard, P., 2009, “Direction Cosine Matrix IMU: Theory,” http://gentlenav.googlecode. com/files/DCMDraft2.pdf
Craig, J. J., 1989, Introduction to Robotics: Mechanics and Control, Addison-Wesley Longman Publishing Co., Inc., Boston, MA.
Kadmiry, B., and Driankov, D., 2004, “A Fuzzy Flight Controller Combining Linguistic and Model-Based Fuzzy Control,” Fuzzy Sets Syst., 146(3), pp. 313–347. [CrossRef]
Bar-Shalom, Y., Li, X. R., and Kirubarajan, T., 2001, Estimation With Applications to Tracking and Navigation: Theory Algorithms and Software, John Wiley & Sons, New York, pp. 200–220.


Grahic Jump Location
Fig. 1

B-frame and E-frame

Grahic Jump Location
Fig. 4

Rate EKF block diagram

Grahic Jump Location
Fig. 5

Rate EKF results in filtering the gyroscope readings

Grahic Jump Location
Fig. 6

Euler EKF block diagram

Grahic Jump Location
Fig. 7

3D simulation environment

Grahic Jump Location
Fig. 8

Pitch angle estimation

Grahic Jump Location
Fig. 9

A zoomed view of pitch angle estimation

Grahic Jump Location
Fig. 10

Roll angle estimation

Grahic Jump Location
Fig. 11

A zoomed view of roll angle estimation

Grahic Jump Location
Fig. 12

A zoomed view of heading angle estimation

Grahic Jump Location
Fig. 13

Monte Carlo test for pitch angle

Grahic Jump Location
Fig. 14

Monte Carlo test for roll angle

Grahic Jump Location
Fig. 15

Monte Carlo test for heading angle




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In