Real-time Image Processing on Low Cost Embedded Computers
Electrical Engineering and Computer Sciences, University of California at Berkeley
University of California at Berkeley, Technical Report No. UCB/EECS-2014-67, 2014
@mastersthesis{Shah:EECS-2014-67,
Author={Shah, Sunil},
Title={Real-time Image Processing on Low Cost Embedded Computers},
School={EECS Department, University of California, Berkeley},
Year={2014},
Month={May},
URL={http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-67.html},
Number={UCB/EECS-2014-67}
}
In 2012 a federal mandate was imposed that required the FAA to integrate unmanned aerial systems (UAS) into the national airspace (NAS) by 2015 for civilian and commercial use. A significant driver for the increasing popularity of these systems is the rise in open hardware and open software solutions which allow hobbyists to build small UAS at low cost and without specialist equipment. This paper describes our work building, evaluating and improving performance of a vision-based system running on an embedded computer onboard such a small UAS. This system utilises open source software and open hardware to automatically land a multi-rotor UAS with high accuracy. Using parallel computing techniques, our final implementation runs at the maximum possible rate of 30 frames per second. This demonstrates a valid approach for implementing other real-time vision based systems onboard UAS using low power, small and economical embedded computers.
May 15, 2014 by hgpu
