Matching and registration between aerial images and prestored road landmarks are critical techniques to enhance unmanned aerial system (UAS) navigation in the global positioning system (GPS)-denied urban environments. Current registration processes typically consist of two separate stages of road extraction and road registration. These two-stage registration approaches are time-consuming and less robust to noise. To that end, in this article, we, for the first time, investigate the problem of end-to-end Aerial-Road registration. Using deep learning, we develop a novel attention-based neural network architecture for Aerial-Road registration. In this model, we construct two-branch neural networks with shared weights to map two input images into a common embedding space. Besides, considering that road features are sparsely distributed in images, we incorporate a novel multibranch attention module to filter out false descriptor matches from the indiscriminative background in order to improve registration accuracy. Finally, the results from extensive experiments show that compared with state-of-the-art approaches, the mean absolute errors of our approach in rotation angle and the translations in the xx - and yy -directions are reduced down by a factor of 1.24, 1.38, and 1.44, respectively. Furthermore, as a byproduct, our experimental results prove the feasibility of a neural network multitask learning approach to simultaneously achieve accurate Aerial-Road matching and registration, thus providing an efficient and accurate UAS geolocalization.

中文翻译：

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基于注意力的道路登记，用于 GPS 拒绝的 UAS 导航


航空图像与预存道路地标之间的匹配和配准是在全球定位系统 (GPS) 无法使用的城市环境中增强无人机系统 (UAS) 导航的关键技术。当前的注册过程通常包括道路提取和道路注册两个独立的阶段。这些两阶段配准方法非常耗时且对噪声的鲁棒性较差。为此，在本文中，我们首次研究了端到端空中道路注册问题。利用深度学习，我们开发了一种新颖的基于注意力的神经网络架构，用于空中-道路配准。在此模型中，我们构建具有共享权重的两分支神经网络，以将两个输入图像映射到公共嵌入空间。此外，考虑到道路特征在图像中分布稀疏，我们采用了一种新颖的多分支注意模块来过滤掉不加区别的背景中的错误描述符匹配，以提高配准精度。最后，大量实验的结果表明，与最先进的方法相比，我们的方法在旋转角度以及 xx 和 yy 方向上的平移的平均绝对误差减少了 1.24 倍，分别为 1.38 和 1.44。此外，作为副产品，我们的实验结果证明了神经网络多任务学习方法同时实现精确的空中-道路匹配和注册的可行性，从而提供高效且准确的无人机地理定位。