Hostname: page-component-cd9895bd7-q99xh Total loading time: 0 Render date: 2024-12-23T01:34:43.927Z Has data issue: false hasContentIssue false
  • English
  • Français

Managing redundant visual measurements for accurate pose tracking

Published online by Cambridge University Press:  02 March 2021

Vincenzo Lippiello  and
Luigi Villani
Vincenzo Lippiello*
Affiliation:
PRISMA Lab., Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)
Luigi Villani*
Affiliation:
PRISMA Lab., Dipartimento di Informatica e Sistemistica, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli (Italy)
*
Email: [email protected]
Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper proposes an algorithm for managing redundant measurements provided by a stereo multi-camera system to achieve accurate visual tracking of a moving object. Selfocclusion, different visual resolution zones and optimal selection of redundant measurements are some of the problems addressed. The algorithm uses the extended Kalman filter embedded in a computational efficient pose estimation procedure based on Binary Space Partitioning Tree geometric modelling of 3D objects. Experimental results are presented for the case of an object moving in the visual space of two fixed cameras.

Keywords

Pose trackingVisual dataRedundant measurementsKalman filter
Type
Research Article
Information
Robotica , Volume 21 , Issue 5 , October 2003 , pp. 511 - 519
Copyright
Copyright © Cambridge University Press 2003

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Wilson, W.J., “Relative end-effector control using cartesian position based visual servoing,” IEEE Trans. on Robotics and Automation, 12, 684–696 (1996).10.1109/70.538974CrossRefGoogle Scholar
2. Janabi-Sharifi, F. and Wilson, W.J., “Automatic grasp planning for visual servo controlled robotic manipulators,” IEEE Trans. on Systems, Man, and Cybernetics-Part B: Cybernetics, 28, 693–711 (1998).10.1109/3477.718519CrossRefGoogle Scholar
3. Lee, S. and Kay, Y., “An accurate estimation of 3-D position and orientation of a moving object for robot stereo vision: Kalman filter approach,” 1990 IEEE Int. Conf. on Robotics and Automation, pp. 414–419 (1990).Google Scholar
4. Wang, J. and Wilson, W.J., “3D relative position and orientation estimation using Kalman filter for robot control,” 1992 IEEE Int. Conf. on Robotics and Automation, pp. 2638–2645 (1992).Google Scholar
5. Pan, J.N., Shi, Y.Q. and Shu, C.Q., “A Kalman filter in motion analysis from stereo image sequence,” 1994 IEEE Int. Conf. on Image Processing, pp. 63–67 (1994).Google Scholar
6. Lippiello, V., Siciliano, B. and Villani, L., “Position and orientation estimation based on Kalman filtering of stereo images,” 2001 IEEE Int. Conf. on Control Applications, pp. 702–707 (2001).Google Scholar
7. Feddema, J.T., Lee, C.S.G. and Mitchell, O.R., “Weighted selection of image features for resolved rate visual feedback control,” IEEE Trans. on Robotics and Automation, 7, 31–47 (1991).10.1109/70.68068CrossRefGoogle Scholar
8. Janabi-Sharifi, F. and Wilson, W.J., “Automatic selection of image features for visual servoing,” IEEE Trans. on Robotics and Automation, 13, 890–903 (1997).10.1109/70.650168CrossRefGoogle Scholar
9. Tarabanis, K., Tsai, R.Y. and Kaul, A., “Computing occlusionfree viewpoints,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 18, 279–292 (1996).10.1109/34.485556CrossRefGoogle Scholar
10. Ho, P.C. and Wang, W., “Occlusion culling using minimum occluder set and opacity map,” 1999 IEEE Int. Conf. on Information Visualization, pp. 292–300 (1999).Google Scholar
11. Drummond, T.W. and Cipolla, R., “Real-time tracking of complex structures with on-line camera calibration,” British Machine Vision Conf., pp. 574–583 (1999).10.5244/C.13.57Google Scholar
12. Weng, J., Cohen, P. and Herniou, M., “Camera calibration with distortion models ad accuracy evaluation,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 14, 965–980 (1992).10.1109/34.159901CrossRefGoogle Scholar
13. Lippiello, V., Siciliano, B. and Villani, L., “3-D objects motion estimation based on Kalman filter and BSP tree models for robot stereo vision,” Archives of Control Sciences, 12, 71–88 (2002).Google Scholar
14. Paterson, M. and Yao, F., “Efficient binary space partitions for hidden-surface removal and solid modeling,” Discrete and Computational Geometry, 5, 485–503 (1990).CrossRefGoogle Scholar
15. Lippiello, V., Siciliano, B. and Villiani, L., “A new method of image features pre-selection for real-time pose estimation based on Kalman filter,” 2002 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 372–377 (2002).Google Scholar
16. Canny, J., “A computational approach to edge detection,” IEEE Trans. on Pattern Analysis and Machine Intelligence, 8, 679–698 (1986).10.1109/TPAMI.1986.4767851CrossRefGoogle Scholar