Many computer vision applications require real-time processing of image data.  This requirement is especially critical for autonomous vehicles performing obstacle avoidance, path planning, and target tracking tasks.  A quickly calculated and relatively rough motion estimate is more useful for autonomous navigation than a more accurate, but slowly calculated estimate. Recent technology advancements in small unmanned air and ground vehicles make many low-cost surveillance and military applications possible. Most of these applications demand a low power, compact, light weight, and high speed computation platform for processing image data in real time. In most cases, the traditional general purpose processor and sequentially executed software approach does not meet these requirements.  In this research, a tensor-based optical flow algorithm is modified and implemented using field programmable gate array (FPGA) for small unmanned vehicle obstacle avoidance and navigation.  We have also implemented two real-time vision algorithms on FPGA.  One is a real-time target tracking algorithm based on the rank transformed image and the other one is a feature density distribution algorithm for time-to-impact estimation.

 Project Sponsors:

 Collaborators:

 Graduate Students:

 Jonathan Anderson and Zhaoyi Wei

1. Z. Wei, D.J. Lee, B.E. Nelson, and M.A. Martineau, “A fast and accurate tensor-based optical flow algorithm implemented in FPGA”, IEEE Workshop on Applications of Computer Vision (WACV 2007), Austin, Texas, USA, Feb 21-22, 2007.

2. J.D. Anderson, D.J. Lee, R.B Schoenberger, and B.J. Tippetts, “Using Real-time Vision to Control a Convoy of Semi-Autonomous Unmanned Vehicle”, AUVSI’s Unmanned Systems North America, online proceedings, Orlando, Florida, August 29-31, 2006.

3. J.D. Anderson, D.J. Lee, and J.K. Archibald, “Hardware Implementation of Feature Density Distribution Algorithm for Autonomous Robot”, Proceedings of The 31^st Annual Conference of the IEEE Industrial Electronics Society (IECON 05), p. 357-362, Raleigh, North Carolina, USA, November 6-10, 2005.

4. D.L. Cardon, W.S. Fife, J.K. Archibald, and D.J. Lee, “Fast 3D Reconstruction for Small Autonomous Robots”, Proceedings of The 31^st Annual Conference of the IEEE Industrial Electronics Society (IECON 05), p. 373-378, Raleigh, North Carolina, USA, November 6-10, 2005.

5. Y. Nagaonkar, B. Call, S. Cluff, J.K. Archibald, and D.J. Lee, “Autonomous Mobile Robotic System with Onboard Vision using Configurable Logic”, Proceedings of The 31^st Annual Conference of the IEEE Industrial Electronics Society (IECON 05), p. 351-356, Raleigh, North Carolina, USA, November 6-10, 2005.

6. J.D. Anderson, D.J. Lee, and J.K. Archibald, “FPGA Implementation of Vision Algorithms for Small Autonomous Robots”, SPIE Optics East, Robotics Technologies and Architectures, Intelligent Robots and Computer Vision XVIII, vol. 6006, p. 401-411, Boston, MA, USA, October 23-26, 2005.