Obstacle avoidance for small UAVs using monocular vision

Jeong-Oog Lee; Keun-Hwan Lee; Sang-Heon Park; Sung-Gyu Im; Jungkeun Park

doi:10.1108/00022661111173270

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Aircraft Engineering and Aerospace Technology

ISSN: 0002-2667

Online from: 1929

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Obstacle avoidance for small UAVs using monocular vision

Document Information:
Title:	Obstacle avoidance for small UAVs using monocular vision
Author(s):	Jeong-Oog Lee, (Aerospace Information Engineering, Konkuk University, Seoul, Republic of Korea), Keun-Hwan Lee, (Aerospace Information Engineering, Konkuk University, Seoul, Republic of Korea), Sang-Heon Park, (Pohang Institute of Intelligent Robotics, Gyeongbuk, Republic of Korea), Sung-Gyu Im, (Aerospace Information Engineering, Konkuk University, Seoul, Republic of Korea), Jungkeun Park, (Aerospace Information Engineering, Konkuk University, Seoul, Republic of Korea)
Citation:	Jeong-Oog Lee, Keun-Hwan Lee, Sang-Heon Park, Sung-Gyu Im, Jungkeun Park, (2011) "Obstacle avoidance for small UAVs using monocular vision", Aircraft Engineering and Aerospace Technology, Vol. 83 Iss: 6, pp.397 - 406
Keywords:	Collisions, Image processing, Monocular vision, MOPS, Obstacle avoidance, SIFT algorithm, Small UAVs
Article type:	Research paper
DOI:	10.1108/00022661111173270 (Permanent URL)
Publisher:	Emerald Group Publishing Limited
Acknowledgements:	This paper was supported by Konkuk University in 2010.
Abstract:	Purpose – The purpose of this paper is to propose that the three-dimensional information of obstacles should be identified to allow unmanned aerial vehicles (UAVs) to detect and avoid obstacles existing in their flight path. Design/methodology/approach – First, the approximate outline of obstacles was detected using multi-scale-oriented patches (MOPS). At the same time, the spatial coordinates of feature points that exist in the internal outline of the obstacles were calculated through the scale-invariant feature transform (SIFT) algorithm. Finally, the results from MOPS and the results from the SIFT algorithm were merged to show the three-dimensional information of the obstacles. Findings – As the method proposed in this paper reconstructs only the approximate outline of obstacles, a quick calculation can be done. Moreover, as the outline information is combined through SIFT feature points, detailed three-dimensional information pertaining to the obstacles can be obtained. Practical implications – The proposed approach can be used efficiently in GPS-denied environments such as certain indoor environments. Originality/value – For the autonomous flight of small UAVs having a payload limit, this paper suggests a means of forming three-dimensional information about obstacles with images obtained from a monocular camera.