Statistically robust 2-D visual servoing

A fundamental step toward broadening the use of real-world image-based visual servoing is to deal with the important issue of reliability and robustness. In order to address this issue, a closed-loop control law is proposed that simultaneously accomplishes a visual servoing task and is robust to a general class of image processing errors. This is achieved with the application of widely accepted statistical techniques such as robust M-estimation and LMedS. Experimental results are presented which demonstrate visual servoing tasks that resist severe outlier contamination.

[1]  Peter I. Corke,et al.  A tutorial on visual servo control , 1996, IEEE Trans. Robotics Autom..

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

[3]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[4]  Peter J. Rousseeuw,et al.  Robust estimation in very small samples , 2002 .

[5]  Charles V. Stewart,et al.  Robust Parameter Estimation in Computer Vision , 1999, SIAM Rev..

[6]  Danica Kragic,et al.  Cue integration for visual servoing , 2001, IEEE Trans. Robotics Autom..

[7]  S B Skaar,et al.  Initial results in the development of a guidance system for a powered wheelchair. , 1996, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[9]  K. Hashimoto,et al.  Visual servoing with redundant features , 1996, Proceedings of 35th IEEE Conference on Decision and Control.

[10]  Andrew I. Comport,et al.  Robust and real-time image-based tracking for markerless augmented reality , 2003 .

[11]  Nikolaos Papanikolopoulos,et al.  Selection of features and evaluation of visual measurements for 3-D robotic visual tracking , 1993, Proceedings of 8th IEEE International Symposium on Intelligent Control.

[12]  Patrick Rives,et al.  Extending visual servoing techniques to nonholonomic mobile robots , 1997, Block Island Workshop on Vision and Control.

[13]  Emanuele Trucco,et al.  Making good features track better , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[14]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[15]  P. Holland,et al.  Robust regression using iteratively reweighted least-squares , 1977 .

[16]  David Suter,et al.  Robust adaptive-scale parametric model estimation for computer vision , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Rachid Deriche,et al.  A Robust Technique for Matching two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry , 1995, Artif. Intell..

[18]  Peter J. Rousseeuw,et al.  Robust Regression and Outlier Detection , 2005, Wiley Series in Probability and Statistics.

[19]  Éric Marchand,et al.  A visual servoing control law that is robust to image outliers , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[20]  A. Hanson,et al.  Scaled Euclidean 3D reconstruction based on externally uncalibrated cameras , 1995, Proceedings of International Symposium on Computer Vision - ISCV.

[21]  James P. Ostrowski,et al.  Visual servoing with dynamics: control of an unmanned blimp , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

[22]  Peter J. Huber,et al.  Robust Statistics , 2005, Wiley Series in Probability and Statistics.

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

[24]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.