In the last few years, squared fiducial markers have become a popular and efficient tool to solve monocular localization and tracking problems at a very low cost. Nevertheless, marker detection is affected by noise and blur: small camera movements may cause image blurriness that prevents marker detection.

The contribution of this paper is two-fold. First, it proposes a novel approach for estimating the location of markers in images using a set of Discriminative Correlation Filters (DCF). The proposed method outperforms state-of-the-art methods for marker detection and standard DCFs in terms of speed, precision, and sensitivity. Our method is robust to blur and scales very well with image resolution, obtaining more than 200fps in HD images using a single CPU thread.

As a second contribution, this paper proposes a method for camera localization with marker maps employing a predictive approach to detect visible markers with high precision, speed, and robustness to blurriness. The method has been compared to the state-of-the-art SLAM methods obtaining, better accuracy, sensitivity, and speed. The proposed approach is publicly available as part of the ArUco library.

在过去的几年中，平方基准标记已经成为一种以非常低的成本解决单眼定位和跟踪问题的流行且有效的工具。但是，标记检测会受到噪声和模糊的影响：相机的小幅移动可能会导致图像模糊，从而无法检测标记。

本文的贡献是双重的。首先，它提出了一种新颖的方法，该方法使用一组判别相关滤波器（DCF）来估计图像中标记的位置。在速度，精度和灵敏度方面，所提出的方法优于最新的标记检测和标准DCF方法。我们的方法具有很强的模糊性，并且可以很好地缩放图像分辨率，使用单个CPU线程即可在高清图像中获得200fps以上的帧率。

作为第二个贡献，本文提出了一种使用标记图进行照相机定位的方法，该方法采用预测方法来检测可见标记，具有较高的精度，速度和对模糊的鲁棒性。该方法已经与获得最新SLAM方法，更好的准确性，灵敏度和速度进行了比较。拟议的方法可作为ArUco库的一部分公开获得。