A position and attitude vision measurement system for wind tunnel slender model

Lei Cheng; Yinong Yang; Bindang Xue; Fugen Zhou; Xiangzhi Bai

doi:10.1117/12.2072604

24 November 2014 A position and attitude vision measurement system for wind tunnel slender model

Lei Cheng, Yinong Yang, Bindang Xue, Fugen Zhou, Xiangzhi Bai

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93012B (2014) https://doi.org/10.1117/12.2072604
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

A position and attitude vision measurement system for drop test slender model in wind tunnel is designed and developed. The system used two high speed cameras, one is put to the side of the model and another is put to the position where the camera can look up the model. Simple symbols are set on the model. The main idea of the system is based on image matching technique between the 3D-digital model projection image and the image captured by the camera. At first, we evaluate the pitch angles, the roll angles and the position of the centroid of a model through recognizing symbols in the images captured by the side camera. And then, based on the evaluated attitude info, giving a series of yaw angles, a series of projection images of the 3D-digital model are obtained. Finally, these projection images are matched with the image which captured by the looking up camera, and the best match’s projection images corresponds to the yaw angle is the very yaw angle of the model. Simulation experiments are conducted and the results show that the maximal error of attitude measurement is less than 0.05°, which can meet the demand of test in wind tunnel.

Citation Download Citation

Lei Cheng, Yinong Yang, Bindang Xue, Fugen Zhou, and Xiangzhi Bai "A position and attitude vision measurement system for wind tunnel slender model", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012B (24 November 2014); https://doi.org/10.1117/12.2072604

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