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

机译:一种基于梯度直方图和点匹配的图像取向估计方法

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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,UAV的应用越来越普遍。作为控制系统的主要研究方向的态度估计引起了工业和学术领域的许多关注。当系统放置在高噪声环境中,传统传感器融合方法的性能显着降低。基于视觉的估计方法,取决于图像几何体,在复杂环境中具有高鲁棒性。本文提出了一种基于梯度方向直方图和点匹配的图像取向估计方法。为了说明所提出的方法的性能,已经进行了几种模拟。第一个实验是基于不同点匹配方法的取向估计。结果是可行的,但在不同的特征描述符方法之间包含大的误差差距。由于匹配样本不良,实时算法具有很大的错误。因此,提出了一种基于梯度方向直方图的测量方法来校正实时点匹配方向估计方法的结果。该方法使用梯度取向直方图滤波器的输出滤波器的纯点匹配方向估计结果。实验结果表明,该方法可以有效地缩小实时匹配方向估计的误差。

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