By Topic

The object recognition and adaptive threshold selection in the vision system for landing an Unmanned Aerial Vehicle

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zeng Fucen ; Dept. of Comput. Sci. & Technol., Tsinghua Univ., Beijing, China ; Shi Haiqing ; Wang Hong

We present the design and implementation of a vision system for landing an Unmanned Aerial Vehicle (UAV). This vision system consists of the vision detection software and the self-made onboard camera platform. After accomplishing its mission, the UAV would return to the helipad and land on it autonomously and accurately. To achieve more, the head of the UAV must point to the direction which the helipad indicates. The object recognition, the main part of our vision algorithm, includes two parts: the recognition of the identifier ldquoHrdquo which indicates the landing point, and the detection of the triangle which indicates the landing direction. To detect the ldquoHrdquo, we designed a new algorithm based on image registration which turn out to be a fast, robust and computational inexpensive method. And to detect the triangle to confirm the landing direction, we used a method based on Hough Line Detection and Helen formula, which is also very fast and accurate. As a breakthrough of our work, we firstly used an adaptive threshold selection method in such a system to make our algorithm more robust. According to the results of our flight trials, our vision system well performed in this application: the average processing time of a 640times480 image is less than 60 ms, and the average rate of successful target detection is 97.42%. In the 2008 Chinese Robotics Competition, we won the Gold Medal of the aerial robotics group.

Published in:

Information and Automation, 2009. ICIA '09. International Conference on

Date of Conference:

22-24 June 2009