Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Barnard, S. T. and M. A. Fischler (1982). Computational stereo. Computing Surveys 14(4), 553–572.
Bolles, R. and J. Woodfill (1993). Spatiotemporal consistency checking of passive range data. Proc ISRR93.
Devernay, F. and O. Faugeras (1995). Automatic calibration and removal of distortion from scenes of structured environments. Proc SPIE95.
Dunn, P. and P. Corke (1997). Real-time stereopsis using FPGAs. FPL97, London.
Eveland, C., K. Konolige and R. C. Bolles (1997). Background Modeling for Segmentation of Video-Rate Stereo Sequences. Submitted to CVPR98.
Faugeras, O. et al. (1993). Real time correlation-based stereo: algorithm, implementations and applications. INRIA Technical Report 2013.
Fua, P. (1993). A parallel stereo algorithm that produces dense depth maps and preserves image features. Machine Vision and Applications 6(1).
Kanada, T. et al. (1996). A stereo machine for video rate dense depth mapping and its new applications. Proc. CVPR96.
Kempainen, S. (1997). CMOS image sensors: eclipsing CCDs in visual information? EDN, October 9, 1997.
Marr, D. and T. Poggio (1979). A computational theory of human stereo vision. Proc. Royal Society B-204, 301–328.
Matthies, L. (1993). Stereo vision for planetary rovers: stochastic modeling to near realtime implementation. IJCV 8(1), 71–91.
Matthies, L., A. Kelly, and T. Litwin (1995). Obstacle detection for unmanned ground vehicles: a progress report. Proc. ISRR.
Moravec, H. P. (1979). Visual mapping by a robot rover. Proc. IJCAI, Tokyo, 598–600.
Nishihara, H. K. (1984). Practical real-time imaging stereo matcher. Optical Engineering 23(5), 536–545.
Point Grey Research (1997) http://www.ptgrey.com.
Woodfill, J. and B. Von Herzen (1997). Real-time stereo vision on the PARTS reconfigurable computer. IEEE Workshop on FPGAs for Custom Computing Machines.
Zabih, R. and J. Woodfill (1994). Non-parametric local transforms for computing visual correspondence. Proc. ECCV94.
Zabih, R. and J. Woodfill (1997). A non-parametric approach to visual correspondence. Submitted to IEEE PAMI.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1998 Springer-Verlag London Limited
About this paper
Cite this paper
Konolige, K. (1998). Small Vision Systems: Hardware and Implementation. In: Shirai, Y., Hirose, S. (eds) Robotics Research. Springer, London. https://doi.org/10.1007/978-1-4471-1580-9_19
Download citation
DOI: https://doi.org/10.1007/978-1-4471-1580-9_19
Publisher Name: Springer, London
Print ISBN: 978-1-4471-1582-3
Online ISBN: 978-1-4471-1580-9
eBook Packages: Springer Book Archive