Adaptive noise identification in vision-assisted motion estimation for unmanned aerial vehicles (original) (raw)

Abstract

Vision localization methods have been widely used in the motion estimation of unmanned aerial vehicles (UAVs). The noise of the vision location result is usually modeled as a white Gaussian noise so that this location result could be utilized as the observation vector in the Kalman filter to estimate the motion of the vehicle. Since the noise of the vision location result is affected by external environment, the variance of the noise is uncertain. However, in previous researches, the variance is usually set as a fixed empirical value, which will lower the accuracy of the motion estimation. The main contribution of this paper is that we proposed a novel adaptive noise variance identification (ANVI) method, which utilizes the special kinematic properties of the UAV for frequency analysis and then adaptively identifies the variance of the noise. The adaptively identified variance is used in the Kalman filter for more accurate motion estimation. The performance of the proposed method is assessed by simulations and field experiments on a quadrotor system. The results illustrate the effectiveness of the method.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. C. S. Yoo, I. K. Ahn. Low cost GPS/INS sensor fusion system for UAV navigation. In Proceedings of the 22nd Digital Avionics Systems Conference, IEEE, Indianapolis, USA, pp. 8.A.1–8.1-9, 2003.
    Google Scholar
  2. F. Aghili, M. Kuryllo, G. Okouneva, C. English. Faulttolerant position/attitude estimation of free-floating space objects using a laser range sensor. IEEE Sensors Journal, vol. 11, no. 1, pp. 176–185, 2011.
    Article Google Scholar
  3. J. F. Vasconcelos, C. Silvestre, P. Oliveira, B. Guerreiro. Embedded UAV model and LASER aiding techniques for inertial navigation systems. Control Engineering Practice, vol. 18, no. 3, pp. 262–278, 2010.
    Article Google Scholar
  4. L. Whitcomb, D. Yoerger, H. Singh. Advances in Doppler-based navigation of underwater robotic vehicles. In Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, Detroit, MI, USA, pp. 399–406, 1999.
    Google Scholar
  5. H. Zhao, Z. Y. Wang. Motion measurement using inertial sensors, ultrasonic sensors, and magnetometers with extended Kalman filter for data fusion. IEEE Sensors Journal, vol. 12, no. 5, pp. 943–953, 2012.
    Article Google Scholar
  6. I. Mondragón, M. Olivares-Méndez, P. Campoy, C. Martínez, L. Mejias. Unmanned aerial vehicles UAVs attitude, height, motion estimation and control using visual systems. Autonomous Robots, vol. 29, no. 1, pp. 17–34, 2010.
    Article Google Scholar
  7. B. Herisse, F. X. Russotto, T. Hamel, R. Mahony. Hovering flight and vertical landing control of a VTOL unmanned aerial vehicle using optical flow. In IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE, Nice, France, pp. 801–806, 2008.
    Google Scholar
  8. M. Bošnak, D. Matko, S. Blažič. Quadrocopter hovering using position-estimation information from inertial sensors and a high-delay video system. Journal of Intelligent & Robotic Systems, vol. 67, pp. 43–60, 2012.
    Article Google Scholar
  9. C. L. Wang, T. M. Wang, J. H. Liang, Y. C. Zhang, Y. Zhou. Bearing-only visual SLAM for small unmanned aerial vehicles in GPS-denied environments. International Journal of Automation and Computing, vol. 10, no. 5, pp. 387–396, 2013.
    Article Google Scholar
  10. F. Zhou, W. Zheng, Z. F. Wang. Adaptive noise variance identification in vision-aided motion estimation. In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, Angers, France, pp. 1–7, 2014.
    Google Scholar
  11. R. Szeliski. Computer Vision: Algorithms and Applications, New York, USA: Springer, pp. 347–348, 2010.
    Google Scholar
  12. M. Achtelik, M. Achtelik, S. Weiss, R. Siegwart. Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments. In Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, Shanghai, China, pp. 3056–3063, 2011.
    Google Scholar
  13. E. Edwan, J. Y. Zhang, J. C. Zhou, O. Loffeld. Reduced DCM based attitude estimation using low-cost IMU and magnetometer triad. In Proceedings of the 8th Positioning Navigation and Communication, IEEE, Dresden, Germany, pp. 1–6, 2011.
    Google Scholar
  14. J. Artieda, J. M. Sebastian, P. Campoy, J. F. Correa, I. F. Mondragn, C. Martnez, M. Olivares. Visual 3-d SLAM from UAVs. Journal of Intelligent and Robotic Systems, vol. 55, no. 4–5, pp. 299–321, 2009.
    Article MATH Google Scholar
  15. K. H. Yang, W. S. Yu, X. Q. Ji. Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera. International Journal of Automation and Computing, vol. 9, no. 3, pp. 292–298, 2012.
    Article Google Scholar
  16. H. G. de Marina, F. J. Pereda, J. M. Giron-Sierra, F. Espinosa. UAV attitude estimation using unscented Kalman filter and TRIAD. IEEE Transactions on Industrial Electronics, vol. 59, no. 11, pp. 4465–4474, 2012.
    Article Google Scholar

Download references

Author information

Authors and Affiliations

  1. Department of Automation, University of Science and Technology of China, Hefei, 230027, China
    Fan Zhou, Wei Zheng & Zeng-Fu Wang
  2. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, 230031, China
    Zeng-Fu Wang

Authors

  1. Fan Zhou
  2. Wei Zheng
  3. Zeng-Fu Wang

Corresponding author

Correspondence toZeng-Fu Wang.

Additional information

This work was supported by National Science and Technology Major Projects of the Ministry of Science and Technology of China: ITER (No. 2012GB102007).

Recommended by Associate Editor Min Tan

Fan Zhou received the B.Eng. degree from University of Science and Technology of China, China in 2009. He is now a Ph.D. candidate in pattern recognition and intelligent system, University of Science and Technology of China, China.

His research interests include unmanned aerial robot, integrated navigation, adaptive signal processing and visual simultaneous localization and mapping.

ORCID iD: 0000-0003-1562-2470

Wei Zheng received the B.Eng. degree from University of Science and Technology of China, China in 2009. He is now a Ph.D. candidate in pattern recognition and intelligent system, University of Science and Technology of China, China.

His research interests include unmanned aerial vehicle, robot localization and navigation, visual simultaneous localization and mapping and multi-sensor fusion.

Zeng-Fu Wang received the B. Sc. degree in electronic engineering from University of Science and Technology of China, China in 1982, and the Ph.D. degree in control engineering from Osaka University, Japan in 1992. He is currently a professor of both Institute of Intelligent Machines, Chinese Academy of Sciences and University of Science and Technology of China. He has published more than 180 journal articles and conference papers.

His research interests include computer vision, human computer interaction and intelligent robots.

ORCID iD: 0000-0003-1859-900X

Rights and permissions

About this article

Cite this article

Zhou, F., Zheng, W. & Wang, ZF. Adaptive noise identification in vision-assisted motion estimation for unmanned aerial vehicles.Int. J. Autom. Comput. 12, 413–420 (2015). https://doi.org/10.1007/s11633-014-0857-7

Download citation

Keywords