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Reconfigurable On-Board Vision Processing for Small Autonomous Vehicles

Abstract

This paper addresses the challenge of supporting real-time vision processing on-board small autonomous vehicles. Local vision gives increased autonomous capability, but it requires substantial computing power that is difficult to provide given the severe constraints of small size and battery-powered operation. We describe a custom FPGA-based circuit board designed to support research in the development of algorithms for image-directed navigation and control. We show that the FPGA approach supports real-time vision algorithms by describing the implementation of an algorithm to construct a three-dimensional (3D) map of the environment surrounding a small mobile robot. We show that FPGAs are well suited for systems that must be flexible and deliver high levels of performance, especially in embedded settings where space and power are significant concerns.

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References

  1. DeSouza GN, Kak AC: Vision for mobile robot navigation: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 2002,24(2):237-267. 10.1109/34.982903

    Article  Google Scholar 

  2. Beard R, Kingston D, Quigley M, et al.: Autonomous vehicle technologies for small fixed-wing UAVs. AIAA Journal of Aerospace Computing, Information, and Communication 2005,2(1):92-108. 10.2514/1.8371

    Article  Google Scholar 

  3. Georgiev A, Allen PK: Localization methods for a mobile robot in urban environments. IEEE Transactions on Robotics 2004,20(5):851-864. 10.1109/TRO.2004.829506

    Article  Google Scholar 

  4. Sáez JM, Escolano F: A global 3D map-building approach using stereo vision. Proceedings of IEEE International Conference on Robotics and Automation (ICRA '04), April 2004, New Orleans, La, USA 2: 1197-1202.

    Google Scholar 

  5. Ruffier F, Franceschini N: Visually guided micro-aerial vehicle: automatic take off, terrain following, landing and wind reaction. Proceedings of IEEE International Conference on Robotics and Automation (ICRA '04), April 2004, New Orleans, La, USA 3: 2339-2346.

    Google Scholar 

  6. Cheng G, Zelinsky A: Real-time visual behaviours for navigating a mobile robot. Proceedings of IEEE International Conference on Intelligent Robots and Systems (IROS '96), November 1996, Osaka, Japan 2: 973-980.

    Article  Google Scholar 

  7. Chang MM, Browning B, Wyeth G: ViperRoos: developing a low cost local vision team for the small size league. In RoboCup 2001: Robot Soccer World Cup V. Springer, Berlin, Germany; 2002:305-311.

    Chapter  Google Scholar 

  8. Bräunl T, Graf B: Autonomous mobile robots with on-board vision and local intelligence. Proceedings of the 2nd IEEE Workshop on Perception for Mobile Agents, Fort Collins (WPMA-2 '99), June 1999, Colorado, Colo, USA 51-57.

    Google Scholar 

  9. Mahlknecht S, Oberhammer R, Novak G: A real-time image recognition system for tiny autonomous mobile robots. Proceedings of IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS '04), May 2004, Toronto, Canada 10: 324-330.

    Google Scholar 

  10. Smith T: Adding vision to Khepera: an autonomous robot footballer, M.S. thesis. University of Sussex, Sussex, UK; 1997.

    Google Scholar 

  11. Bertin P, Roncin D, Vuillemin J: Introduction to programmable active memories. In Tech. Rep. PRL-RR-3. DEC Paris Research Laboratory, Paris, France; 1989.

    Google Scholar 

  12. Hirai S, Zakouji M, Tsuboi T: Implementing image processing algorithms on FPGA-based realtime vision systems. Proceedings of the 11th Workshop on Synthesis and System Integration of Mixed Information Technologies (SASIMI '03), April 2003, Hiroshima, Japan 378-385.

    Google Scholar 

  13. McBader S, Lee P: An FPGA implementation of a flexible, parallel image processing architecture suitable for embedded vision systems. Proceedings of the 17th IEEE International Symposium on Parallel and Distributed Processing (IPDPS '03), April 2003, Nice, France 228.

    Google Scholar 

  14. Darabiha A, Rose J, MacLean WJ: Video-rate stereo depth measurement on programmable hardware. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, June 2003, Madison, Wis, USA 1: 203-210.

    Google Scholar 

  15. Jia Y, Zhang X, Li M, An L: A miniature stereo vision machine (MSVM-III) for dense disparity mapping. Proceedings of the 17th IEEE International Conference on Pattern Recognition (ICPR '04), August 2004, Cambridge, UK 1: 728-731.

    Google Scholar 

  16. Wong SC, Jasiunas M, Kearney D: Towards a reconfigurable tracking system. Proceedings of IEEE International Conference on Field Programmable Logic and Applications (FPL '05), August 2005, Tampere, Finland 456-462.

    Google Scholar 

  17. Yamada H, Tominaga T, Ichikawa M: An autonomous flying object navigated by real-time optical flow and visual target detection. Proceedings of IEEE International Conference on Field-Programmable Technology (FPT '03), December 2003, Tokyo, Japan 222-227.

    Google Scholar 

  18. Díaz J, Ros E, Pelayo F, Ortigosa EM, Mota S: FPGA-based real-time optical-flow system. IEEE Transactions on Circuits and Systems for Video Technology 2006,16(2):274-279.

    Article  Google Scholar 

  19. Arribas PC: Real time hardware vision system applications: optical flow and time to contact detector units. Proceedings of the IEEE International Caracas Conference on Devices, Circuits and Systems (ICCDCS '04), November 2004, Punta Cana, Dominican Republic 281-288.

    Google Scholar 

  20. Rincon AM, Lee WR, Slattery M: The changing landscape of system-on-a-chip design. Proceedings of the IEEE Custom Integrated Circuits, May 1999, San Diego, Calif, USA 83-90.

    Google Scholar 

  21. Bergamaschi RA, Bhattacharya S, Wagner R, et al.: Automating the design of SOCs using cores. IEEE Design and Test of Computers 2001,18(5):32-45. 10.1109/54.953270

    Article  Google Scholar 

  22. Gwennap L: Comparing embedded processors. January 2005, http://www.embedded.com

  23. Thrun S: Robotic mapping: a survey. In Exploring Artificial Intelligence in the New Millenium. Edited by: Lakemeyer G, Nevel B. Morgan Kaufmann, San Francisco, Calif, USA; 2002:1-35.

    Google Scholar 

  24. Cardon DL, Fife WS, Archibald JK, Lee DJ: Fast 3D reconstruction for small autonomous robots. Proceedings of the 31st Annual Conference of IEEE Industrial Electronics Society (IECON '05), November 2005, Raleigh, NC, USA 373-378.

    Google Scholar 

  25. Harris C, Stephens M: A combined corner and edge detector. Proceedings of the 4th Alvey Vision Conference, August 1988, Manchester, UK 147-151.

    Google Scholar 

  26. Ma Y, Soatto S, Kosecka J, Shastry SS: An Invitation to 3D Vision. Springer, New York, NY, USA; 2004.

    Book  Google Scholar 

  27. Lucas BD, Kanade T: An iterative image registration technique with an application to stereoscopic vision. Proceedings of the 7th International Joint Conference on Artificial Intelligence (IJCAI '81), August 1981, Vancouver, BC, Canada 674-679.

    Google Scholar 

  28. Shi J, Tomasi C: Good features to track. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '94), June 1994, Seattle, Wash, USA 593-600.

    Google Scholar 

  29. Jin H, Favaro P, Soatto S: Real-time feature tracking and outlier rejection with changes in illumination. Proceedings of 8th IEEE International Conference on Computer Vision (ICCV '01), July 2001, Vancouver, BC, Canada 1: 684-689.

    Google Scholar 

  30. IEEE standard for binary floating-point arithmetic ANSI/IEEE Std. 754-1985, 1985

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Correspondence to Wade S Fife.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (https://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Fife, W.S., Archibald, J.K. Reconfigurable On-Board Vision Processing for Small Autonomous Vehicles. J Embedded Systems 2007, 080141 (2006). https://doi.org/10.1155/2007/80141

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