A Rotational and Translational Image Stabilization System for Remotely Operated Robots

Remotely operated robots equipped with on board cameras, apart from providing video input to operators, perform optical measurements to assist their navigation as well. Such image processing algorithms require image sequences, free of high frequency unwanted movements, in order to generate their optimal results. Image stabilization is the process which removes the undesirable position fluctuations of a video sequence improving, therefore, its visual quality. In this paper, we introduce the implementation of an image stabilization system that utilizes input from an on board camera and a gyrosensor. The frame sequence is processed by an optic flow algorithm and the inertial data is processed by a discrete Kalman filter. The compensation is performed using two servo motors for the pan and tilt movements and frame shifting for the vertical and horizontal movements. Experimental results of the robot head, have shown fine stabilized image sequences and a system capable of processing 320 times 240 pixel image sequences at approximately 10 frames/sec, with a maximum acceleration of A deg/sec2.

[1]  David J. Fleet,et al.  Performance of optical flow techniques , 1994, International Journal of Computer Vision.

[2]  Ryo Kurazume,et al.  Development of image stabilization system for remote operation of walking robots , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  Dario Floreano,et al.  Fly-inspired visual steering of an ultralight indoor aircraft , 2006, IEEE Transactions on Robotics.

[4]  Chin-Teng Lin,et al.  A robust digital image stabilization technique based on inverse triangle method and background detection , 2005, IEEE Transactions on Consumer Electronics.

[5]  Jean-Yves Bouguet,et al.  Camera calibration toolbox for matlab , 2001 .

[6]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[7]  J. Paik,et al.  An Adaptive Motion Decision System For Digital Image Stabilizer Based On Edge Pattern Matching , 1992, IEEE 1992 International Conference on Consumer Electronics Digest of Technical Papers.

[8]  Jorge Dias,et al.  Vision and Inertial Sensor Cooperation Using Gravity as a Vertical Reference , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  J J Koenderink,et al.  Affine structure from motion. , 1991, Journal of the Optical Society of America. A, Optics and image science.

[10]  Seppo J. Ovaska,et al.  Angular acceleration measurement: a review , 1998, IEEE Trans. Instrum. Meas..

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

[12]  Giulio Sandini,et al.  Visuo-inertial stabilization in space-variant binocular systems , 2000, Robotics Auton. Syst..

[13]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[14]  Emanuele Trucco,et al.  Introductory techniques for 3-D computer vision , 1998 .

[15]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .