Small Vision Systems: Hardware and Implementation

Robotic systems are becoming smaller, lower power, and cheaper, enabling their application in areas not previously considered. This is true of vision systems as well. SRI’s Small Vision Module (SVM) is a compact, inexpensive realtime device for computing dense stereo range images, which are a fundamental measurement supporting a wide range of computer vision applications. We describe hardware and software issues in the construction of the SVM, and survey implemented systems that use a similar area correlation algorithm on a variety of hardware.

[1]  Hans P. Moravec.  Visual Mapping by a Robot Rover , 1979, IJCAI.

[2]  Oliver. Faugeras,et al.  Real time correlation based stereo algorithm and application , 1993 .

[3]  Ramin Zabih,et al.  A non-parametric approach to visual correspondence , 1996 .

[4]  Alonzo Kelly,et al.  Obstacle detection for unmanned ground vehicles: a progress report , 1995 .

[5]  Ramin Zabih,et al.  Non-parametric Local Transforms for Computing Visual Correspondence , 1994, ECCV.

[6]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[7]  Peter Corke,et al.  Real-time stereopsis using FPGAs , 1997, TENCON '97 Brisbane - Australia. Proceedings of IEEE TENCON '97. IEEE Region 10 Annual Conference. Speech and Image Technologies for Computing and Telecommunications (Cat. No.97CH36162).

[8]  Olivier Faugeras,et al.  Automatic calibration and removal of distortion from scenes of structured environments , 1995, Optics & Photonics.

[9]  Takeo Kanade,et al.  A stereo machine for video-rate dense depth mapping and its new applications , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[10]  Martin A. Fischler,et al.  Computational Stereo , 1982, CSUR.

[11]  John Woodfill,et al.  Real-time stereo vision on the PARTS reconfigurable computer , 1997, Proceedings. The 5th Annual IEEE Symposium on Field-Programmable Custom Computing Machines Cat. No.97TB100186).

[12]  R. Bolles,et al.  Spatiotemporal Consistency Checking of Passive Renge Data , 1993 .

[13]  Robert C. Bolles,et al.  Background modeling for segmentation of video-rate stereo sequences , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[14]  H. K. Nishihara,et al.  Practical Real-Time Imaging Stereo Matcher , 1984 .

[15]  D Marr,et al.  A computational theory of human stereo vision. , 1979, Proceedings of the Royal Society of London. Series B, Biological sciences.