Luminance: A New Visual Feature for Visual Servoing

This chapter is dedicated to a new way to achieve robotic tasks by 2D visual servoing. Contrary to most of related works in this domain where geometric visual features are usually used, we directly here consider the luminance of all pixels in the image. We call this new visual servoing scheme photometric visual servoing. The main advantage of this new approach is that it greatly simplifies the image processing required to track geometric visual features all along the camera motion or to match the initial visual features with the desired ones. However, as it is required in classical visual servoing, the computation of the so-called interaction matrix is required. In our case, this matrix links the time variation of the luminance to the camera motions.We will see that this computation is based on a illumination model able to describe complex luminance changes. However, since most of the classical control laws fail when considering the luminance as a visual feature, we turn the visual servoing problem into an optimization one leading to a new control law. Experimental results on positioning tasks validate the feasibility of photometric visual servoing and show its robustness regarding to approximated depths, Lambertian and non Lambertian objects, low textured objects, partial occlusions and even, to some extent, to image content.

[1]  Ezio Malis,et al.  Improving vision-based control using efficient second-order minimization techniques , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  François Chaumette,et al.  Visual Servoing Based on Image Motion , 2001, Int. J. Robotics Res..

[3]  Danica Kragic,et al.  Advances in robot vision , 2005, Robotics Auton. Syst..

[4]  Giulio Sandini,et al.  Visual Behaviors for Docking , 1997, Comput. Vis. Image Underst..

[5]  Éric Marchand,et al.  Statistically robust 2-D visual servoing , 2006, IEEE Transactions on Robotics.

[6]  J. Reichman Determination of absorption and scattering coefficients for nonhomogeneous media. 1: theory. , 1973, Applied optics.

[7]  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.

[8]  C. V. Jawahar,et al.  Visual servoing based on Gaussian mixture models , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Christophe Collewet,et al.  Modeling complex luminance variations for target tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  James F. Blinn,et al.  Models of light reflection for computer synthesized pictures , 1977, SIGGRAPH.

[11]  Hidenori Kimura,et al.  LQ OPTIMAL AND NONLINEAR APPROACHES TO VISUAL SERVOING , 1993 .

[12]  Omar Tahri,et al.  On the efficient second order minimization and image-based visual servoing , 2008, 2008 IEEE International Conference on Robotics and Automation.

[13]  François Chaumette,et al.  Visual Servoing and Visual Tracking , 2008, Springer Handbook of Robotics.

[14]  Nikolaos Papanikolopoulos,et al.  Selection of features and evaluation of visual measurements during robotic visual servoing tasks , 1995, J. Intell. Robotic Syst..

[15]  Patrick Rives,et al.  A new approach to visual servoing in robotics , 1992, IEEE Trans. Robotics Autom..

[16]  Bui Tuong Phong Illumination for computer generated pictures , 1975, Commun. ACM.

[17]  William J. Wilson,et al.  Automatic selection of image features for visual servoing , 1997, IEEE Trans. Robotics Autom..

[18]  Patrick Bouthemy,et al.  Exploiting Image Motion for Active Vision in a Visual Servoing Framework , 1996, Int. J. Robotics Res..

[19]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

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

[21]  Éric Marchand,et al.  Feature tracking for visual servoing purposes , 2005, Robotics Auton. Syst..

[22]  Giulio Sandini,et al.  Camera self orientation and docking maneuver using normal flow , 1995, Defense, Security, and Sensing.

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

[24]  Selim Benhimane,et al.  Homography-based 2D Visual Tracking and Servoing , 2007, Int. J. Robotics Res..

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