A reduced complexity vision system for autonomous helicopter navigation

Abstract

Many current avenues of vision research involve fully analyzing an image with expensive, high powered computers. This approach has major implications in terms of cost, size, and power consumption. Other methods have involved sub-sampling an image to reduce cost and complexity. This has the disadvantage of information loss. We present a low cost, low powered, reduced complexity vision system capable of intelligently sampling an image to reduce this information loss. The design philosophy and methodology is discussed, along with sample applications. Primarily we demonstrate how the reduced complexity vision system will be used to aid in navigation of an autonomous flying vehicle. This is quantified by showing how having multiple sampling schemes result in increased robustness and accuracy of our helicopter line tracking algorithm.