A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.
College and Department
Ira A. Fulton College of Engineering and Technology; Electrical and Computer Engineering
BYU ScholarsArchive Citation
Tippetts, Beau J., "Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV" (2008). Theses and Dissertations. 1418.
real-time image processing, micro-UAV, control, quad-rotor, on-board, vision system, target tracking, BYU, FPGA, RANSAC, similarity-constrained homography, template matching, Helio-copter