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영상기반 편대비행을 위한 선도기 자세예측 알고리즘

Pose Estimation of Leader Aircraft for Vision-based Formation Flight

  • 투고 : 2013.03.27
  • 심사 : 2013.06.24
  • 발행 : 2013.07.01

초록

본 논문은 편대비헹에서 영상만을 이용하여 선도기의 자세를 예측 하는 알고리즘 개발에 대해 논하고 있다. X-PLANE 시뮬레이터를 이용하여 획득한 영상에 SURF(Speed Up Robust Features)알고리즘을 이용하여 특징점을 추출 하였다. 그리고 자세예측 방법은 POSIT(Pose from Orthography and Scaling with Iteration) 알고리즘을 사용하였다. 결론적으로 우리는 영상만을 이용한 자세추정법이 $1.1{\sim}1.76^{\circ}$의 작은 추정오차 결과를 나타냄을 확인할 수 있었다.

This paper describes a vision-based only attitude estimation technique for the leader in the formation flight. The feature points in image obtained from the X-PLANE simulator are extracted by the SURF(Speed Up Robust Features) algorithm. We use POSIT(Pose from Orthography and Scaling with Iteration) algorithm to estimate attitude. Finally we verify that attitude estimation using vision only can yield small estimated error of $1.1{\sim}1.76^{\circ}$.

키워드

참고문헌

  1. Seung-Min Oh and Eric N. Johnson., " Relative Motion Estimation for Vision-based Formation Flight using Unscented Kalman Filter," AIAA Guidance, Navigation and Control Conference and Exhibit, 20 - 23. Aug. 2007
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  5. Yeong-Ju Kim, Jun-Yong Lee, Hyun-Joo Park, Dongjin Lee, and Hyochoong Bang "Real-time Implementation of Vision-based Pose Estimation for UAV Aerial Refueling" KSAS Spring Symposium, 2011, pp. 633-637
  6. Minku Kang, Wonkook Choo and Seungbin Moon, "Face Recognition based on SURF Interest Point Extraction Algorithm." IEEK, Vol. 48, No. 3, 2011, pp.323-330
  7. Deok-Jae Seo, Hyun-Woo Kim, Young-Mo Kim, Doo-Hyun Choi, and Re-Mi Do, "Vehicle detection using SURF." CICS, 2010, pp.107-108
  8. Ji-Wook Kwon, Mun-Soo Park, Dongkyoung Chwa, and Suk-Kyo Hong, "Three-Dimensional Pose Estimaion of Neighbor Mobile Robots in Formation System Based on the Vision System", ICROS, Vol. 15, No. 12, 2009, pp.1223-1231 https://doi.org/10.5302/J.ICROS.2009.15.12.1223

피인용 문헌

  1. Monocular Vision-Based Guidance and Control for a Formation Flight vol.16, pp.4, 2015, https://doi.org/10.5139/IJASS.2015.16.4.581