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Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera

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Abstract

The rotation matrix estimation problem is a keypoint for mobile robot localization, navigation, and control. Based on the quaternion theory and the epipolar geometry, an extended Kalman filter (EKF) algorithm is proposed to estimate the rotation matrix by using a single-axis gyroscope and the image points correspondence from a monocular camera. The experimental results show that the precision of mobile robot’s yaw angle estimated by the proposed EKF algorithm is much better than the results given by the image-only and gyroscope-only method, which demonstrates that our method is a preferable way to estimate the rotation for the autonomous mobile robot applications.

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References

  1. A. Chatfield. Fundamentals of High Accuracy Inertial Navigation, USA: American Institute of Aeronautics and Astronautics, 1997.

    Google Scholar 

  2. B. Hofmann-Wellenhof, H. Lichtenegger, J. Collins. Global Positioning System: Theory and Practice, Berlin, Germany: Springer-Verlag, 2001.

    Book  Google Scholar 

  3. D. C. K. Yuen, B. A. MacDonald. Vision-based localization algorithm based on landmark matching, triangulation, reconstruction, and comparison. IEEE Transactions on Robotics, vol. 21, no. 2, pp. 217–226, 2005.

    Article  Google Scholar 

  4. S. Das, N. Vaswani. Nonstationary shape activities: Dynamic models for landmark shape change and applications. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 4, pp. 579–592, 2010.

    Article  Google Scholar 

  5. P. Sala, R. Sim, A. Shokoufandeh, S. Dickinson. Landmark selection for vision-based navigation. IEEE Transactions on Robotics, vol. 22, no. 2, pp. 334–349, 2006.

    Article  Google Scholar 

  6. D. Xu, M. Tan, X. G. Zhao, Z. G. Tu. Seam tracking and visual control for robotic arc welding based on structured light stereovision. International Journal of Automation and Computing, vol. 1, no. 1, pp. 63–75, 2004.

    Article  Google Scholar 

  7. P. Yang, W. Wu, M. Moniri, C. C. Chibelushi. A sensorbased SLAM algorithm for camera tracking in virtual studio. International Journal of Automation and Computing, vol. 5, no. 2, pp. 152–162, 2008.

    Article  Google Scholar 

  8. X. Wei, M. Jane. Robust relative pose estimation with integrated cheirality constraint. In Proceedings of the 19th IEEE International Conference on Pattern Recognition, IEEE, Tampa, USA, pp. 1–4, 2008.

    Google Scholar 

  9. J. Kosecka, X. Yang. Global localization and relative pose estimation based on scale-invariant features. In Proceedings of the 17th IEEE International Conference on Pattern Recognition, IEEE, Cambridge, UK, vol. 4, pp. 319–322, 2004.

    Chapter  Google Scholar 

  10. D. Nister. An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 26, no. 6, pp. 756–777, 2004.

    Article  Google Scholar 

  11. D. Strelow. Motion Estimation from Image and Inertial Measurements, Ph.D. dissertation, CMU-CS-04-178, School of Computer Science, Carnegie Mellon University, USA, 2004.

    Google Scholar 

  12. A. Huster, S. M. Rock. Relative position sensing by fusing monocular vision and inertial rate sensors. In Proceedings of the 11th International Conference on Advanced Robotics, IEEE, Coimbra, Portugal, pp. 1562–1567, 2003.

    Google Scholar 

  13. E. Foxlin, Y. Altshuler, L. Naimark, M. Harrington. Flight-Tracker: A novel optical/inertial tracker for cockpit enhanced vision. In Proceedings of the 3rd IEEE and ACM International Symposium on Mixed and Augmented Reality, IEEE, Arlington, USA, pp. 212–221, 2004.

    Chapter  Google Scholar 

  14. H. Rehbinder, B. K. Ghosh. Pose estimation using linebased dynamic vision and inertial sensors. IEEE Transactions on Automatic Control, vol. 48, no. 2, pp. 186–199, 2003.

    Article  MathSciNet  Google Scholar 

  15. J. Lobo, J. Dias. Vision and inertial sensor cooperation using gravity as a vertical reference. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1597–1608, 2003.

    Article  Google Scholar 

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

    Google Scholar 

  17. S. L. Altmann. Rotations, Quaternions, and Double Groups, UK: Oxford University Press, 1986.

    MATH  Google Scholar 

  18. R. Hartley, A. Zisserman. Multiple View Geometry in Computer Vision, UK: Cambridge University Press, 2004.

    Book  MATH  Google Scholar 

  19. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.

    Article  Google Scholar 

Download references

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Authors and Affiliations

Authors

Corresponding author

Correspondence to Ke-Hu Yang.

Additional information

This work was supported by National Natural Science Foundation of China (Nos. 60874010 and 61070048), Innovation Program of Shanghai Municipal Education Commission (No. 11ZZ37), Fundamental Research Funds for the Central Universities (No. 009QJ12), and Collaborative Construction Project of Beijing Municipal Commission of Education.

Ke-Hu Yang received the B. Sc. degree in measurement and control technology and instrumentation from Northwestern Polytechnical University, Xi’an, PRC in 2003, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, Beijing, PRC in 2009. Then he joined the School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, PRC.

His research interests include industrial control theories and applications, intelligent robot navigation and control, and power electronics.

Wen-Sheng Yu received the Ph.D. degree in dynamics and control from Peking University, Beijing, RPC in 1998. Then he joined the Institute of Automation, Chinese Academy of Sciences, Beijing, PRC, where he was promoted from assistant researcher to associate professor in 1999, to professor in 2004. He was a visiting research fellow in University of Melbourne from 2001 to 2002. Since 2009, he has been professor in Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai, PRC. He is an author/coauthor of numerous articles in journals and conference proceedings, which span the fundamental fields of control theory and control engineering and applied mathematics.

His research interests include robust and optimal control, adaptive filter theory and linear estimation, control theory and control engineering, robotics and control, complex systems and control, signal processing and communications, algorithms research for robust control, and mechanization for control theory.

Xiao-Qiang Ji is a master student at the School of Mechanical Electronic and Information Engineering, China University of Mining and Technology, Beijing, PRC. His major is measurement and control technology and instrumentation.

His research interests include intelligent control and intelligent robotics.

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Yang, KH., Yu, WS. & Ji, XQ. Rotation estimation for mobile robot based on single-axis gyroscope and monocular camera. Int. J. Autom. Comput. 9, 292–298 (2012). https://doi.org/10.1007/s11633-012-0647-z

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  • DOI: https://doi.org/10.1007/s11633-012-0647-z

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