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An Image Orientation Estimation Method Based on Gradient Histogram and Point Matching

机译:基于梯度直方图和点匹配的图像方向估计方法

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In recent years, the application of UAV has been more and more widespread. Attitude estimation as the main research direction of the control system has attracted much attention from industrial and academic area. The performance of traditional sensor fusion method is degraded significantly when the system is placed in high noise environments. Vision based estimation method, which depends on image geometry, has high robustness in complex environment. In this paper, we propose an image orientation estimation method based on gradient orientation histogram and point matching. In order to illustrate the performance of the proposed method, several simulations have been carried out. The first experiment is orientation estimation based on different point matching methods. The results are feasible, but contain a big error gap between different feature descriptor methods. The real-time algorithm has a large error because of the bad matching samples. Therefore a measurement method based on gradient orientation histogram is proposed to correct the results of real-time point matching orientation estimation method. This method use the output of gradient orientation histogram filter the results of pure point matching orientation estimation. The experiment results show that the proposed method can effectively shrink the error of real-time point matching orientation estimation.
机译:近年来,无人机的应用越来越广泛。姿态估计作为控制系统的主要研究方向已经引起了工业界和学术界的广泛关注。当系统置于高噪声环境中时,传统传感器融合方法的性能会大大降低。基于视觉的估计方法取决于图像的几何形状,在复杂的环境中具有很高的鲁棒性。本文提出一种基于梯度方向直方图和点匹配的图像方向估计方法。为了说明所提出方法的性能,已经进行了一些仿真。第一个实验是基于不同点匹配方法的方向估计。结果是可行的,但是在不同的特征描述符方法之间存在很大的误差。由于匹配样本不正确,实时算法具有较大的误差。因此,提出了一种基于梯度方向直方图的测量方法,对实时点匹配方向估计方法的结果进行校正。该方法使用梯度方向直方图的输出过滤纯点匹配方向估计的结果。实验结果表明,该方法可以有效地缩小实时点匹配方位估计的误差。

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