北京航空航天大学学报 ›› 2006, Vol. 32 ›› Issue (11): 1349-1353.

• 论文 • 上一篇    下一篇

基于扩展卡尔曼滤波的舰机相对位姿估测

王睿, 阎鹏, 刘红英, 张广军   

  1. 1. 北京航空航天大学 仪器科学与光电工程学院, 北京 100083;
    2. 北京航空航天大学 理学院, 北京 100083
  • 收稿日期:2006-04-30 出版日期:2006-11-30 发布日期:2010-09-19
  • 作者简介:王 睿(1965-),女,北京人,副教授,wangr@buaa.edu.cn.
  • 基金资助:

    航空基金资助项目(03I51009)

Visual 3D motion estimation of UAV and landing target based on extended Kalman filter

Wang Rui, Yan Peng, Liu Hongying, Zhang Guangjun   

  1. 1. School of Instrument Science and Opto-electronics Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100083, China;
    2. School of Science, Beijing University of Aeronautics and Astronautics, Beijing 100083, China
  • Received:2006-04-30 Online:2006-11-30 Published:2010-09-19

摘要: 通过将基于扩展卡尔曼滤波的长序列图像分析方法与单目视觉技术相结合,把无人机自主着舰视觉导引中舰机间相对位姿的估测,转化为机载摄像机对着舰靶标平面3D位姿的实时估测问题.首先根据透视投影理论,建立了以摄像机的透镜中心为原点且Z轴与光轴重合的摄像机坐标系和世界坐标系,然后利用机载摄像机连续拍摄的靶标图像序列,选择描述相对运动的3个欧拉角、平移向量及它们的速度作为状态变量;由靶标角点的提取和帧间匹配,建立了反映着舰靶标上特征点的图像坐标和状态变量之间关系的观测方程,带入扩展卡尔曼滤波器,估测出舰机的相对运动参数.计算机数据仿真和基于DSP平台的半实物仿真试验验证了算法的有效性和鲁棒性.

Abstract: During the last phase of auto landing an UAV (unmanned aerial vehicle) on the ship, the estimation of the 3D relative motion parameters between UAV and the landing target can be regarded as the planar 3D motion estimation between the camera mounted on the UAV and the deck. An algorithm for visual motion estimation of 3D objects based on extended Kalman filter is presented. First, the camera coordinate with the origin at the camera′s lens and the world coordinate are set up appealing to the principles of perspective projection. Then, the actual 3D camera motion parameters (the three Eulerian angles, transition vectors and their velocities) can be described in terms of the state equation. Furthermore, with the target corner extraction and frame matching, the observation equation is proposed to give the relationship of the feature points in the image and the state vectors. All the 3D relative motion parameters are solved by the stated EKF(extended Kalman filter) method. The presented experimental results of both synthetic data and the real image sequences show that our algorithm is effectively and robust.

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