A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system

Abstract

Visual tracking, as a popular computer vision technique, has a wide range of applications, such as camera pose estimation. Conventional methods for it are mostly based on vision only, which are complex for image processing due to the use of only one sensor. This paper proposes a novel sensor fusion algorithm fusing the data from the camera and the fiber-optic gyroscope. In this system, the camera acquires images and detects the object directly at the beginning of each tracking stage; while the relative motion between the camera and the object measured by the fiber-optic gyroscope can track the object coordinate so that it can improve the effectiveness of visual tracking. Therefore, the sensor fusion algorithm presented based on the tracking system can overcome the drawbacks of the two sensors and take advantage of the sensor fusion to track the object accurately. In addition, the computational complexity of our proposed algorithm is obviously lower compared with the existing approaches (86% reducing for a 0.5 min visual tracking). Experiment results show that this visual tracking system reduces the tracking error by 6.15% comparing with the conventional vision-only tracking scheme (edge detection), and our proposed sensor fusion algorithm can achieve a long-term tracking with the help of bias drift suppression calibration.

This is a preview of subscription content, access via your institution.

References

  1. 1

    Klein G S W, Drummond T W. Tightly integrated sensor fusion for robust visual tracking. Image Vision Comput, 2004, 22: 769–776

    Article  Google Scholar 

  2. 2

    Hol J D. Pose Estimation and Calibration Algorithms for Vision and Inertial Sensors. Sweden Linköping: Linköping Univ Press, 2008. 7–23

    Google Scholar 

  3. 3

    Song S, Qiao W, Li B, et al. An efficient magnetic tracking method using uniaxial sensing coil. IEEE Trans Magn, 2014, 50: 4003707

    Google Scholar 

  4. 4

    Wang Q, Chen W P, Zheng R, et al. Acoustic target tracking using tiny wireless sensor devices. Inf Process Sens Netw, 2003, 2634: 642–657

    Article  MATH  Google Scholar 

  5. 5

    Zhang X, Hu W, Xie N, et al. A robust tracking system for low frame rate video. Int J Comput Vis, 2015, 115: 279–304

    MathSciNet  Article  Google Scholar 

  6. 6

    Zhang X, Hu W, Maybank S, et al. Sequential particle swarm optimization for visual tracking. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Anchorage, 2008. 1–8

    Google Scholar 

  7. 7

    Li Y, Ai H, Yamashita T, et al. Tracking in low frame rate video: a cascade particle filter with discriminative observers of different life spans. IEEE Trans Anal, 2008, 30: 1728–1740

    Google Scholar 

  8. 8

    Park I K, Singhal N, Lee M H, et al. Design and performance evaluation of image processing algorithms on GPUs. IEEE Trans Parall Distr, 2011, 22: 91–104

    Article  Google Scholar 

  9. 9

    Erdem A T, Ercan A O. Fusing inertial sensor data in an extended Kalman filter for 3D camera tracking. IEEE Trans Image Process, 2015, 24: 538–548

    MathSciNet  Article  Google Scholar 

  10. 10

    Foxlin E. Inertial head-tracker sensor fusion by a complementary separate-bias Kalman filter. In: Proceedings of IEEE Virtual Reality Annual International Symposium, Santa Clara, 1996. 185–194

    Google Scholar 

  11. 11

    Hol J D, Dijkstra F, Luinge H, et al. Tightly coupled UWB/IMU pose estimation. In: Proceedings of IEEE International Conference on Ultra-Wideband, Vancouver, 2009. 688–692

    Google Scholar 

  12. 12

    He P, Cardou P, Desbiens A, et al. Estimating the orientation of a rigid body moving in space using inertial sensors. Multibody Syst Dyn, 2014, 35: 1–27

    MathSciNet  MATH  Google Scholar 

  13. 13

    Corke P, Lobo J, Dias J. An introduction to inertial and visual sensing. Int J Robot Res, 2007, 26: 519–535

    Article  Google Scholar 

  14. 14

    Starner T. Project glass: an extension of the self. IEEE Pervas Comput, 2013, 12: 14–16

    Article  Google Scholar 

  15. 15

    Chai L, Nguyen K, Hoff B, et al. An adaptive estimator for registration in augmented reality. In: Proceedings of 2nd IEEE and ACM International Workshop on Augmented Reality, San Francisco, 1999. 23–32

    Google Scholar 

  16. 16

    You S, Neumann U. Fusion of vision and gyro tracking for robust augmented reality registration. In: Proceedings of IEEE Conference on Virtual Reality, Yokohama, 2001. 71–78

    Google Scholar 

  17. 17

    Zhang G. The Principles and Technologies of Fiber-Optic Gyroscope. Beijing: National Defense Industry Press, 2008. 1–25

    Google Scholar 

  18. 18

    Barbour N, Schmidt G. Inertial sensor technology trends. IEEE Sens J, 2001, 1: 332–339

    Article  Google Scholar 

  19. 19

    Hwangbo M, Kim J S, Kanade T. Gyro-aided feature tracking for a moving camera: fusion, auto-calibration and GPU implementation. Int J Robot Res, 2011, 30: 1755–1774

    Article  Google Scholar 

  20. 20

    Yu J, Wang Z F. 3D facial motion tracking by combining online appearance model and cylinder head model in particle filtering. Sci China Inf Sci, 2014, 57: 029101

    Google Scholar 

  21. 21

    Gonzalez R C, Woods R E, Eddins S L. Digital Image Processing Using Matlab. Beijing: Publishing House of Electronics Industry, 2014. 205–229

    Google Scholar 

  22. 22

    Wang X, He C, Wang Z. Method for suppressing the bias drift of interferometric all-fiber optic gyroscopes. Opt Lett, 2011, 36: 1191–1193

    Article  Google Scholar 

  23. 23

    He C, Yang C, Wang Z. Fusion of finite impulse response filter and adaptive Kalman filter to suppress angle random walk of fiber optic gyroscopes. Opt Eng, 2012, 51: 124401–124401

    Article  Google Scholar 

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Chuanchuan Yang.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Tan, Z., Yang, C., Li, Y. et al. A low-complexity sensor fusion algorithm based on a fiber-optic gyroscope aided camera pose estimation system. Sci. China Inf. Sci. 59, 042412 (2016). https://doi.org/10.1007/s11432-015-5516-2

Download citation

Keywords

  • visual tracking
  • camera pose estimation
  • fiber-optic gyroscope
  • low-complexity
  • sensor fusion