Motivated by the 2013 International UAV Innovation Grand Prix, we design and implement a real-time vision system for an unmanned helicopter autonomously transferring cargoes between two platforms. In the competition, four cargoes are initially placed inside four circles on one platform, respectively. They are required to be transferred one by one into the four circles on the other platform. This paper presents the core algorithms of the proposed vision system on ellipse detection, ellipse tracking, and single-circle-based position estimation. Experiments and the great success of our team in the competition have verified the efficiency, accuracy, and robustness of the algorithms. Our team was ranked first in the final round competition.Index Terms-Circle-based pose estimation, ellipse detection, ellipse tracking, unmanned aerial vehicle (UAV), vision-based navigation.

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To address the problem of deviation and registration of 3D radar and infrared sensor, this paper presents and improves a method based on the state value and space deviation of federated filtering of unscented Kalman filter and standard Kalman filter, which conduces to real time registering of system deviation of radar and IF sensors. In the method presented here, a covariance matching criteria-based approach was employed for judgment of filtering divergent trend, while self-adaptive attenuation factor was introduced for correction of the predicted error covariance so as to inhibit the divergent phenomenon. The experiment results indicated that the method presented here conduces to improvement of the precision and speed of space registration, showing practical value in deviation registration of 3D radars and infrared sensors.

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