Fan Zhou, Wei Zheng and Zeng-Fu Wang. Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles. International Journal of Automation and Computing, vol. 12, no. 4, pp. 413-420, 2015. https://doi.org/10.1007/s11633-014-0857-7
Citation: Fan Zhou, Wei Zheng and Zeng-Fu Wang. Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles. International Journal of Automation and Computing, vol. 12, no. 4, pp. 413-420, 2015. https://doi.org/10.1007/s11633-014-0857-7

Adaptive Noise Identification in Vision-assisted Motion Estimation for Unmanned Aerial Vehicles

doi: 10.1007/s11633-014-0857-7
Funds:

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

  • Received Date: 2014-01-23
  • Rev Recd Date: 2014-04-02
  • Publish Date: 2015-08-01
  • 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.

     

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  • [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, 2003.
    [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.
    [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.
    [4]
    L. Whitcomb, D. Yoerger, H. Singh. Advances in dopplerbased navigation of underwater robotic vehicles. In Proceedings of the IEEE International Conference on Robotics and Automation, IEEE, Detroit, MI, USA, pp. 399-406, 1999.
    [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.
    [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.
    [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.
    [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.
    [9]
    C. L. Wang, T. M. Wang, J. H. Liang, Y. C. Zhang, Y. Zhou. Bearing-only visual SLAM for small unmanned aerialvehicles in GPS-denied environments. International Journal of Automation and Computing, vol. 10, no. 5, pp. 387-396, 2013.
    [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.
    [11]
    R. Szeliski. Computer Vision: Algorithms and Applications, New York, USA: Springer, pp. 347-348, 2010.
    [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.
    [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.
    [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.
    [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.
    [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.
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