Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor

Shanjun Chen; Haibin Duan; Yimin Deng; Cong Li; Guozhi Zhao; Yan Xu

doi:10.1117/1.OE.56.12.124105

12 December 2017 Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor

Shanjun Chen, Haibin Duan, Yimin Deng, Cong Li, Guozhi Zhao, Yan Xu

Author Affiliations +

Optical Engineering, Vol. 56, Issue 12, 124105 (December 2017). https://doi.org/10.1117/1.OE.56.12.124105

Abstract

Autonomous aerial refueling is a significant technology that can significantly extend the endurance of unmanned aerial vehicles. A reliable method that can accurately estimate the position and attitude of the probe relative to the drogue is the key to such a capability. A drogue pose estimation method based on infrared vision sensor is introduced with the general goal of yielding an accurate and reliable drogue state estimate. First, by employing direct least squares ellipse fitting and convex hull in OpenCV, a feature point matching and interference point elimination method is proposed. In addition, considering the conditions that some infrared LEDs are damaged or occluded, a missing point estimation method based on perspective transformation and affine transformation is designed. Finally, an accurate and robust pose estimation algorithm improved by the runner-root algorithm is proposed. The feasibility of the designed visual measurement system is demonstrated by flight test, and the results indicate that our proposed method enables precise and reliable pose estimation of the probe relative to the drogue, even in some poor conditions.

Citation Download Citation

Shanjun Chen, Haibin Duan, Yimin Deng, Cong Li, Guozhi Zhao, and Yan Xu "Drogue pose estimation for unmanned aerial vehicle autonomous aerial refueling system based on infrared vision sensor," Optical Engineering 56(12), 124105 (12 December 2017). https://doi.org/10.1117/1.OE.56.12.124105

Received: 25 June 2017; Accepted: 14 November 2017; Published: 12 December 2017

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