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A new calibration method for an inertial and visual sensing system

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Abstract

The relative pose between inertial and visual sensors equipped in autonomous robots is calibrated in two steps. In the first step, the sensing system is moved along a line, the orientations in the relative pose are computed from at least five corresponding points in the two images captured before and after the movement. In the second step, the translation parameters in the relative pose are obtained with at least two corresponding points in the two images captured before and after one step motion. Experiments are conducted to verify the effectiveness of the proposed method.

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References

  1. P. Corke, J. Lobo, J. Dias. An introduction to inertial and visual sensing. International Journal of Robotics Research, vol. 26, no. 6, pp. 519–535, 2007.

    Article  Google Scholar 

  2. Y. Wu, D. Hu, M. P. Wu, X. Hu, T. Wu. Observability analysis of rotation estimation by fusing inertial and line-based visual information: A revisit. Automatica, vol. 42, no. 10, pp. 1809–1812, 2006.

    Article  MATH  Google Scholar 

  3. A. Hasan, K. Samsudin, A. R. Ramli. Intelligently tuned wavelet parameters for GPS/INS error estimation. International Journal of Automation and Computing, vol. 8, no. 4, pp. 411–420, 2011.

    Article  Google Scholar 

  4. A. D. Cabrol, T. Garcia, P. Bonnin, M. Chetto. A concept of dynamically reconfigurable real-time vision system for autonomous mobile robotics. International Journal of Automation and Computing, vol. 5, no. 2, pp. 174–184, 2008.

    Article  Google Scholar 

  5. F. Labrosse. The visual compass: Performance and limitations of an appearance-based method. Journal of Field Robotics, vol. 23, no. 10, pp. 913–941, 2006.

    Article  Google Scholar 

  6. S. J. Yu, S. R. Sukumar, A. F. Koschan, D. L. Page, M. A. Abidi. 3D reconstruction of road surfaces using an integrated multi-sensory approach. Optics and Lasers in Engineering, vol. 45, no. 7, pp. 808–818, 2007.

    Article  Google Scholar 

  7. J. C. Zufferey, D. Floreano. Fly-inspired visual steering of an ultralight indoor aircraft. IEEE Transactions on Robotics, vol. 22, no. 1, pp. 137–146, 2006.

    Article  Google Scholar 

  8. S. Viollet, N. Franceschini. A high speed gaze control system based on the vestibulo-ocular reflex. Robotics and Autonomous Systems, vol. 50, no. 4, pp. 147–161, 2005.

    Article  Google Scholar 

  9. 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 

  10. 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 

  11. V. Gupta, S. Brennan. Terrain-based vehicle orientation estimation combining vision and inertial measurements. Journal of Field Robotics, vol. 25, no. 3, pp. 181–202, 2008.

    Article  Google Scholar 

  12. N. Trawny, A. I. Mourikis, S. I. Roumeliotis, A. E. Johnson, J. F. Montgomery. Vision-aided inertial navigation for pin-point landing using observations of mapped landmarks. Journal of Field Robotics, vol. 24, no. 5, pp. 357–378, 2007.

    Article  Google Scholar 

  13. J. Kim, S. Sukkarieh. Real-time implementation of airborne inertial-SLAM. Robotics and Autonomous Systems, vol. 55, no. 1, pp. 62–71, 2007.

    Article  Google Scholar 

  14. S. M. Abdallah, D. C. Asmar, J. S. Zelek. A benchmark for outdoor vision SLAM systems. Journal of Field Robotics, vol. 24, no. 1–2, pp. 145–165, 2007.

    Article  Google Scholar 

  15. M. Bryson, S. Sukkarieh. Building a robust implementation of bearing-only inertial SLAM for a UAV. Journal of Field Robotics, vol. 24, no. 1–2, pp. 113–143, 2007.

    Article  Google Scholar 

  16. R. M. Eustice, L. H. Singh, J. J. Leonard, M. R. Walter. Visually mapping the RMS Titanic: Conservative covariance estimates for SLAM information filters. International Journal of Robotics Research, vol. 25, no. 12, pp. 1223–1242, 2006.

    Article  Google Scholar 

  17. P. Gemeiner, P. Einramhof, M. Vincze. Simultaneous motion and structure estimation by fusion of inertial and vision data. International Journal of Robotics Research, vol. 26, no. 6, pp. 591–605, 2007.

    Article  Google Scholar 

  18. Y. Q. Tao, H. S. Hu. A novel sensing and data fusion system for 3-D arm motion tracking in telerehabilitation. IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 5, pp. 1029–1040, 2008.

    Article  Google Scholar 

  19. F. Ababsa, M. Mallem. Hybrid three-dimensional camera pose estimation using particle filter sensor fusion. Advanced Robotics, vol. 21, no. 1–2, pp. 165–181, 2007.

    Article  Google Scholar 

  20. J. Lobo, J. Dias. Relative pose calibration between visual and inertial sensors. International Journal of Robotics Research, vol. 26, no. 6, pp. 561–575, 2007.

    Article  Google Scholar 

  21. F. M. Mirzaei, S. I. Roumeliotis. A Kalman filter-based algorithm for IMU-camera calibration: Observability analysis and performance evaluation. IEEE Transactions on Robotics, vol. 24, no. 5, pp. 1143–1156, 2008.

    Article  Google Scholar 

  22. K. G. Derpanis. The Harris Corner Detector, Technical Report, York University, Canada, 2004.

    Google Scholar 

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Authors and Affiliations

  1. State Key Laboratory of Intelligent Control and Management of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, PRC

    De Xu & Min Tan

  2. East China Research Institute of Electronic Engineering, Hefei, 230088, PRC

    Hua-Wei Wang

  3. Department of Manufacturing Engineering and Engineering Management, City University of Hong Kong, Hong Kong, PRC

    You-Fu Li

Authors
  1. De Xu
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  2. Hua-Wei Wang
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  3. You-Fu Li
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  4. Min Tan
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Corresponding author

Correspondence to De Xu.

Additional information

This work was supported by National Natural Science Foundation of China (Nos. 60805038 and 60725309) and Beijing Natural Science Foundation (No. 4082032).

De Xu received the B. Sc. and M. Sc. degrees from Shandong University of Technology, PRC in 1985 and 1990, respectively, and the Ph.D. degree from Zhejiang University, PRC in 2001, all in control science and engineering. Since 2001, he has been with the Institute of Automation, Chinese Academy of Sciences (IACAS), PRC. He is currently a professor with the State Key Laboratory of Intelligent Control and Management of Complex Systems, IACAS.

His research interests include robotics and automation, especially the control of robots such as visual-control and intelligent control.

Hua-Wei Wang received the B. Sc. degree in mechanical design and theory from Beihang University, PRC in 2004, and the M. Sc. degree in mechanical design and theory from Beijing University of Technology, PRC in 2007. He received his Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences, PRC in 2010. He is now with East China Research Institute of Electronic Engineering, PRC.

His research interests include mechatronics, robotics and automation.

You-Fu Li received the B. Sc. and M. Sc. degrees in electrical engineering from the Harbin Institute of Technology, PRC in 1982 and 1986, respectively, and the Ph.D. degree in robotics from Oxford University, UK in 1993. From 1993 to 1995, he worked as a postdoctoral researcher in the Department of Computer Science, University of Wales, UK. He joined City University of Hong Kong in 1995. He has published over 100 papers in refereed international journals and conferences. He is an associate editor of IEEE Transactions on Automation Science and Engineering.

His research interests include robot vision, visual tracking, robot sensing and sensor-based control, mechatronics, and automation.

Min Tan received the B. Sc. degree from Tsinghua University, PRC in 1986, and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences (IACAS), PRC in 1990, both in control science and engineering. He is currently a professor with the State Key Laboratory of Intelligent Control and Management of Complex Systems, IACAS. He has published over 100 papers in journals, books, and conferences.

His research interests include robotics and intelligent control system.

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Xu, D., Wang, HW., Li, YF. et al. A new calibration method for an inertial and visual sensing system. Int. J. Autom. Comput. 9, 299–305 (2012). https://doi.org/10.1007/s11633-012-0648-y

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  • DOI: https://doi.org/10.1007/s11633-012-0648-y

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