New results in images moments-based visual servoing

In image-based visual servoing, the choice of visual features has a strong influence on the performance of the control system. In the last decade, image-moments have been exploited in several visual servoing schemes for their ability to represent object region, object defined by contours or a set of discrete points. Despite the many recent advances, the choice of moment-based features to control the most critical Degrees Of Freedom (DOF), i.e those used to control the rotational motions around the x-axis and y-axis of the camera remains a key issue. In this paper, a new feature formula to control these critical DOF that does not depend on the object shape is proposed. The new features are computed from shifted moments and selected in order to provide nice invariant properties. Additionally, low order shifted moments can be exploited to minimize the impact of measurement noise on the control performances.

[1]  Roland T. Chin,et al.  On Image Analysis by the Methods of Moments , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  François Chaumette,et al.  Complex Objects Pose Estimation based on Image Moment Invariants , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[3]  François Chaumette,et al.  New decoupled visual servoing scheme based on invariants from projection onto a sphere , 2008, 2008 IEEE International Conference on Robotics and Automation.

[4]  Christophe Collewet,et al.  Colorimetry-based visual servoing , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Christophe Collewet,et al.  Photometry-based visual servoing using light reflexion models , 2009, 2009 IEEE International Conference on Robotics and Automation.

[6]  M. Teague.  Image analysis via the general theory of moments , 1980 .

[7]  Nicolas Mansard,et al.  Task Sequencing for High-Level Sensor-Based Control , 2007, IEEE Transactions on Robotics.

[8]  Alireza Khotanzad,et al.  Invariant Image Recognition by Zernike Moments , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Christophe Collewet,et al.  Photometric Visual Servoing , 2011, IEEE Transactions on Robotics.

[10]  François Chaumette,et al.  Image moments: a general and useful set of features for visual servoing , 2004, IEEE Transactions on Robotics.

[11]  Peter I. Corke,et al.  A new partitioned approach to image-based visual servo control , 2001, IEEE Trans. Robotics Autom..

[12]  Christophe Collewet,et al.  Positioning a camera with respect to planar objects of unknown shape by coupling 2-D visual servoing and 3-D estimations , 2002, IEEE Trans. Robotics Autom..

[13]  Chee-Way Chong,et al.  Translation and scale invariants of Legendre moments , 2004, Pattern Recognit..

[14]  Peter I. Corke,et al.  Generic decoupled image-based visual servoing for cameras obeying the unified projection model , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Koichiro Deguchi,et al.  A Direct Interpretation of Dynamic Images with Camera and Object Motions for Vision Guided Robot Control , 2000, International Journal of Computer Vision.

[16]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

[17]  Hiroshi Murase,et al.  Subspace methods for robot vision , 1996, IEEE Trans. Robotics Autom..

[18]  François Chaumette,et al.  Determination of Moment Invariants and Their Application to Visual Servoing , 2003 .

[19]  Rafik Mebarki,et al.  Automatic guidance of robotized 2D ultrasound probes with visual servoing based on image moments. , 2010 .

[20]  Gregory D. Hager,et al.  Kernel-based visual servoing , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  R. Mukundan,et al.  Moment Functions in Image Analysis: Theory and Applications , 1998 .

[22]  François Chaumette,et al.  Point-based and region-based image moments for visual servoing of planar objects , 2005, IEEE Transactions on Robotics.