Many computer vision applications require realtime 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 paper, a tensorbased optical flow algorithm is modified and implemented using field programmable gate array (FPGA) for small unmanned vehicle obstacle avoidance and navigation.