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A Model-aided Optical Flow/Inertial Sensor Fusion Method for a Quadrotor

Published online by Cambridge University Press:  12 August 2016

Pin Lyu ,
Jizhou Lai ,
Hugh H.T. Liu ,
Jianye Liu  and
Wenjing Chen
Pin Lyu
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China) (Institute for Aerospace Studies, University of Toronto, Toronto, Canada)
Jizhou Lai*
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Hugh H.T. Liu
Affiliation:
(Institute for Aerospace Studies, University of Toronto, Toronto, Canada)
Jianye Liu
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
Wenjing Chen
Affiliation:
(College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China)
*
(E-mail: [email protected])
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Abstract

In this paper, a fault-tolerant velocity estimation method is proposed for quadrotors in a GPS denied environment. A novel filter is developed in light of the quadrotor model and measurements from optical flow and inertial sensors. The proposed filter is capable of detecting and isolating the optical flow sensor faults, by which the velocity estimation accuracy and stability will be improved. It is also demonstrated that the wind velocity is observable in the proposed filter. Therefore, the new filter can also be implemented in a windy environment, which is a significant improvement to the previous model-aided inertial sensor estimator. At the end, some simulations are carried out to verify the advantages of our method.

Keywords

State estimationModel-aided navigationOptical flowMultirotor
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 2 , March 2017 , pp. 325 - 341
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Abeywardena, D., Kodagoda, S., Dissanayake, G. and Munasinghe, R. (2013a). Improved state estimation in quadrotor mavs: A novel drift-free velocity estimator. IEEE Robotics and Automation Magazine, 20, 32–39.CrossRefGoogle Scholar
Abeywardena, D., Wang, Z., Dissanayake, G., Waslander, S.L. and Kodagoda, S. (2014). Model-aided state estimation for quadrotor micro air vehicles amidst wind disturbances. IEEE/RSJ International Conference on Intelligent Robots and Systems, Chicago, IL.Google Scholar
Abeywardena, D., Wang, Z., Kodagoda, S. and Dissanayake, G. (2013b). Visual-inertial fusion for quadrotor micro air vehicles with improved scale observability. IEEE International Conference on Robotics and Automation, German.Google Scholar
Arain, B. and Kendoul, F. (2014). Real-time wind speed estimation and compensation for improved flight. IEEE Transactions on Aerospace and Electronic Systems, 50, 1599–1606.Google Scholar
Artinez, V. (2007). Modelling of the flight dynamics of a quadrotorhelicopter. Master's thesis, Cranfield University, England.Google Scholar
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J. and Szeliski, R. (2011). A database and evaluation methodology for optical flow. International Journal of Computer Vision, 92, 1–31.Google Scholar
Bangura, M. and Mahony, R. (2012). Nonlinear dynamic modeling for high performance control of a quadrotor. Australasian conference on robotics and automation, New Zealand.Google Scholar
Baranek, R. and Solc, F. (2014). Model-based attitude estimation for multicopters. Advances in Electrical and Electronic Engineering, 12, 501–510.CrossRefGoogle Scholar
Bristeau, P.J., Callou, F., Vissiere, D. and Petit, N. (2011). The navigation and control technology inside the ar. drone micro uav. 18th IFAC World Congress, Italy.Google Scholar
Bristeau, P.J., Martin, P., Salaun, E. and Petit, N. (2009). The role of propeller aerodynamics in the model of a quadrotor uav. Proceedings of the European Control Conference, Hungary.Google Scholar
Brox, T. and Malik, J. (2011). Large displacement optical flow: descriptor matching in variational motion estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33, 500–513.Google Scholar
Bouabdallah, S. (2007). Design and control of quadrotors with application to autonomous flying. PhD thesis, École Polytechnique federale de Lausanne, Switzerland.Google Scholar
Crocoll, P., Seibold, J., Scholz, G. and Trommer, G.F. (2014). Model-aided navigation for a quadrotor helicopter: A novel navigation system and first experimental results. Navigation, 61, 253–271.Google Scholar
Gupte, S., Mohandas, P.I.T. and Conrad, J.M. (2012). A survey of quadrotor unmanned aerial vehicles. Proceedings of IEEE Southeastcon, Orlando, FL.Google Scholar
Hanley, D.J. (2015). An improved model-based observer for inertial navigation for quadrotors with low cost imus . Master's thesis, University of Illinois at Urbana-Champaign, Illinois, USA.Google Scholar
Hoffmann, G.M., Huang, H., Waslander, S.L. and Tomlin, C.J. (2007). Quadrotor helicopter flight dynamics and control: Theory and experiment. AIAA Guidance, Navigation, and Control Conference and Exhibit, South Carolina.Google Scholar
Kaya, D. and Kutay, A.T. (2015). Modeling and simulation of a quadrotor using curve fitting method. AIAA Atmospheric Flight Mechanics Conference, Dallas, TX.Google Scholar
Kelly, J. and Sukhatme, G.S. (2011). Visual-inertial sensor fusion: Localization, mapping and sensor-to-sensor self-calibration. The International Journal of Robotics Research, 30, 56–79.CrossRefGoogle Scholar
Langelaan, J.W., Alley, N. and Neidhoefer, J. (2011). Wind field estimation for small unmanned aerial vehicles. Journal of Guidance, Control, and Dynamics, 34, 1016–1030.Google Scholar
Lv, P., Lai, J.Z., Liu, J.Y. and Nie, M.X. (2014). The compensation effects of gyros’ stochastic errors in a rotational inertial navigation system. The Journal of Navigation, 67, 1069–1088.Google Scholar
Leishman, R.C., McLain, T.W. and Beard, R.W. (2014a). Relative navigation approach for vision-based aerial gps-denied navigation. Journal of Intelligent & Robotic Systems, 74, 97–111.Google Scholar
Leishman, R.C., Macdonald, J.C., Beard, R.W. and McLain, T.W. (2014b). Quadrotors and accelerometers: State estimation with an improved dynamic model. IEEE Control Systems agazine, 34, 28–41.Google Scholar
Macdonald, J.C., Leishman, R.C., Beard, R.W. and McLain, T.W. (2014). Analysis of an improved imu-based observer for multirotor helicopters. Journal of Intelligent & Robotic Systems, 74, 1049–1061.Google Scholar
Mahony, R., Kumar, V. and Corke, P. (2012). Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robotics & amp amp Automation Magazine, 19, 20–32.Google Scholar
Martin, P. and Salaun, E. (2010). The true role of accelerometer feedback in quadrotor control. IEEE International Conference on Robotics and Automation, Anchorage.Google Scholar
Mirzaei, F.M. and Roumeliotis, S. (2008). A kalman filter-based algorithm for imu-camera calibration: Observability analysis and performance evaluation. IEEE Transactions on Robotics, 24, 1143–1156.Google Scholar
Powers, C., Mellinger, D., Kushleyev, A., Kothmann, B. and Kumar, V. (2013). Influence of aerodynamics and proximity effects in quadrotor flight. Proceedings of the 13th International Symposium on Experimental Robotics, Canada.Google Scholar
Sun, D., Roth, S. and Black, M.J. (2014). A quantitative analysis of current practices in optical low estimation and the principles behind them. International Journal of Computer Vision, 106, 115–137.Google Scholar
Tabbache, B., Benbouzid, M.E.H., Kheloui, A. and Bourgeot, J. (2013). Virtual-sensor-based maximum-likelihood voting approach for fault-tolerant control of electric vehicle powertrains. IEEE Transactions on Vehicular Technology, 62, 1075–1083.CrossRefGoogle Scholar
Theys, B., Dimitriadis, G., Andrianne, T., Hendrick, P. and Schutter, J.D. (2014). Wind tunnel testing of a vtol mav propeller in tilted operating mode. International Conference on Unmanned Aircraft Systems, Orlando, FL.Google Scholar
Waslander, S.L. and Wang, C. (2009). Wind disturbance estimation and rejection for quadrotor position control. AIAA Infotech Aerospace Conference, Washington.CrossRefGoogle Scholar