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基于四旋翼飞行器的改进金字塔LK光流算法的研究 被引量:3

Research on optical flow algorithm of improved Pyramid LK based on quadrotor
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摘要 针对四旋翼飞行器在飞行过程的水平漂移问题,提出利用改进的金字塔LK光流算法予以改善。首先,通过Visual Studio2013仿真,确知改进的金字塔LK光流算法比传统金字塔LK光流算法对图像的速度信息提取精度更高。然后,再建立四旋翼飞行器的动力学模型,将两种光流算法导入MATLAB的仿真模型中,得出:四旋翼飞行器能有效根据改进的金字塔LK光流算法获得速度信息以降低水平漂移。最后,通过在轴距为430 mm的四旋翼飞行器的平台上进行飞行试验,结果表明:采用改进的金字塔LK光流算法能使四旋翼飞行器的水平漂移程度降低,可实现较为稳定的悬停。 Aiming at the horizontal drift problem of quadrotor,optical flow algorithm of improved Pyramid LK is used to solve it.First of all,through the simulation which founded by Visual Studio2013,The author know that the optical flow algorithm of improved Pyramid LK is more accuracy than optical flow of traditional Pyramid LK for extracting the image of speed. After that,dynamics model of quadrotor is established,then leading the two algorithms into MATLAB simulation model and the outcome reveal that quadrotor can obtain information of the velocity effectively according to the optical flow algorithm of improved Pyramid LK. Finally,through flight test on the quadrotor which the wheelbase is 430 mm. The results showed that the optical flow algorithm of improved pyramid LK can effectively reduce the horizontal drift of the quadrotor and achieve a more stable hover.
出处 《电视技术》 北大核心 2017年第7期110-115,共6页 Video Engineering
基金 国家自然科学基金项目(71361014) 江西省重点科技计划项目(20151BBE50038)
关键词 四旋翼飞行器 水平漂移 改进的金字塔LK光流算法 稳定悬停 quadrotor horizontal drift optical flow algorithm of improved Pyramid LK stable hover
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  • 1Toshev A,Makadia A,Daniilidis K.Shape-based object recog- nition in videos using 3D synthetic object models[C]//2009 IEEE Conference on Computer Vision and Pattern Recogni- tion, 2009 : 288-295.
  • 2Lucas B D,Kanade T.An iterative image registration tech- nique with an application to stereo vision[C]//Proceedings of Imaging Understanding Workshop.USA: Morgan Kaufmann Publishers Inc, 1981 : 121-130.
  • 3McCane B,Novins K.On benchmarking optical flow[J].Com- puter Vision and Image Understanding, 2001,84( 1 ) : 126-143.
  • 4Horn K P, Schunck G.Determining optical flow[J].Artificial Intelligence, 1981,17( 1 ) : 185-203.
  • 5Tao M,Bai Jiamin.SimpleFlow:a non-iterative,sublinear opti- cal flow algorithm[J].Computer Graphics Forum, 2012,31 (2) :345-353.
  • 6Lowe D G.Distinctive image features from scale-invariant key- points[J].International Journal of Computer Vision, 2004, 60 (2):91-110.
  • 7Gauglitz S, Htillerer T.Evaluation of interest point detectors and feature descriptors for visual tracking[J].International Jour- nal of Computer Vision,2011,94(3) :335-360.
  • 8Brown M, Lowe D G.Automatic panoramic image stitching using invariant features[J].International Journal of Computer Vision, 2007,74( 1 ) : 59-73.
  • 9Gao Tao,Li Guo,Lian Shiguo.Tracking video objects with feature points based particle filtering[J].Multimedia Tools and Applications,2012,58( 1 ) : 1-21.
  • 10Matthews I,Ishikawa T,Baker S.The template update prob- lem[J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004,26 (6) : 810-815.

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