Active Vision - Rectification and Depth Mapping

We present a mapping approach to scene understanding based on active stereo vision. We generalise traditional static multi-camera rectification techniques to enable active epipolar rectification and intuitive representation of the results. The approach is shown to enable the use of any static stereo algorithms with active multi-camera systems. In particular, we show the use of the framework to apply static depthmapping techniques to the active case. Further, we outline the benefits of using an occupancy grid framework for the fusion and representation of range data, and find that it is especially suited for active vision. Finally, we provide a preview of our approach to dynamic occupancy grids for scene understanding.

[1]  Ernst D. Dickmanns,et al.  An Expectation-based, Multi-focal, Saccadic (EMS) Vision System for Vehicle Guidance , 2000 .

[2]  Allen R. Hanson,et al.  Obstacle Detection Based on Qualitative and Quantitative 3D Reconstruction , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  A. Verri,et al.  A compact algorithm for rectification of stereo pairs , 2000 .

[4]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[5]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.

[6]  Peter I. Corke,et al.  Quantitative Evaluation of Matching Methods and Validity Measures for Stereo Vision , 2001, Int. J. Robotics Res..

[7]  Dana H. Ballard,et al.  Animate Vision , 1991, Artif. Intell..

[8]  Hans P. Moravec,et al.  Robot Evidence Grids. , 1996 .

[9]  Alexander Zelinsky,et al.  A Novel Mechanism for Stereo Active Vision , 2000 .

[10]  R. Bajcsy Active perception , 1988 .

[11]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[12]  James J. Little,et al.  Vision-based mobile robot localization and mapping using scale-invariant features , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[13]  Masayuki Inaba,et al.  Real time 3D depth flow generation and its application to track walking human beings , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[14]  Mubarak Shah,et al.  Recovering 3D Motion of Multiple Objects Using Adaptive Hough Transform , 1997, IEEE Trans. Pattern Anal. Mach. Intell..