北京航空航天大学学报 ›› 2011, Vol. 37 ›› Issue (7): 849-854.

• 论文 • 上一篇    下一篇

用于飞行器视觉导航的地平线检测算法

付丙岛1, 丁文锐2, 王广彪3, 李红光2   

  1. 1. 北京航空航天大学 电子信息工程学院, 北京 100191;
    2. 北京航空航天大学 无人驾驶飞行器设计研究所, 北京 100191;
    3. 北京航空航天大学 电子信息工程学院, 北京 100191
  • 收稿日期:2010-09-10 出版日期:2011-07-30 发布日期:2011-08-04
  • 作者简介:付丙岛 (1986-), 男, 山东济南人,硕士生, fubingdao@126.com.
  • 基金资助:

    航空科学基金资助项目(2008ZC1029)

Vehicle visual navigation algorithm based on detect of horizon

Fu Bingdao1, Ding Wenrui2, Wang Guangbiao3, Li Hongguang2   

  1. 1. School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    2. Research Institute of Unmanned Aerial Vehicle, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
    3. School of Electronics and Information Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China
  • Received:2010-09-10 Online:2011-07-30 Published:2011-08-04

摘要: 结合航拍视频序列的特点与视觉导航系统的需求,提出了一种基于图像区域相似性和禁忌搜索算法的地平线拟合视觉导航算法.从航拍视频序列中提取单帧图像,并做下采样去噪等相应预处理;初始化禁忌搜索算法的初始解等参数,结合适配值函数,拟合图像中的地平线;用禁忌搜索算法的特赦准则和收敛准则评判拟合的准确性,直到适配值函数值满足收敛准则为止,得出最优解;分解出飞行器的横滚角和俯仰角,并由横滚角和俯仰角的大小决定下次参数校正的间隔,输出结果给控制系统.实验结果表明,与传统视觉导航方法和基于Hough变换的方法相比,此算法具有无参考、实时性好和抗噪性强等特点.

Abstract: Combined with the demand of vehicle’s visual navigation system and the features of aerial images, a horizon detection algorithm which is used by visual navigation and based on the similarity between regions of images and tabu search was proposed. A single frame extracted from aerial video sequences was delt with downsampling and noise reduction. Tabu search algorithm parameters, such as initial solution, were initialized. Tabu search algorithm combined with the fit function fit the position of horizon. Tabu search-s amnesty and convergence criteria were used to judge the value of fit function until it meet convergence criteria. The values of the bank angle and the pitch angle which decide the next parameter-adjusting interval, and send the results to control system were driven. Compared with the traditional visual navigation method and the methods based on Hough transform, the experimental results show that the proposed algorithm is no-reference, good real-time and robust.

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