In this article, we aim to explore the potential of using onboard cameras and pre-stored geo-referenced imagery for Unmanned Aerial Vehicle (UAV) localization. Such a vision-based localization enhancing system is of vital importance, particularly in situations where the integrity of the global positioning system (GPS) is in question (i.e., in the occurrence of GPS outages, jamming, etc.). To this end, we propose a complete trainable pipeline to localize an aerial image in a pre-stored orthomosaic map in the context of UAV localization. The proposed deep architecture extracts the features from the aerial imagery and localizes it in a pre-ordained, larger, and geotagged image. The idea is to train a deep learning model to find neighborhood consensus patterns that encapsulate the local patterns in the neighborhood of the established dense feature correspondences by introducing semi-local constraints. We qualitatively and quantitatively evaluate the performance of our approach on real UAV imagery. The training and testing data is acquired via multiple flights over different regions. The source code along with the entire dataset, including the annotations of the collected images has been made public. ^{1 1}

https://github.com/m-hamza-mughal/Aerial-Template-Matching.

Up-to our knowledge, such a dataset is novel and first of its kind which consists of 2052 high-resolution aerial images acquired at different times over three different areas in Pakistan spanning a total area of around 2 km $^2$ .

中文翻译：

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通过深度上下文图像匹配协助无人机定位

在本文中，我们旨在探索将机载摄像头和预存储的地理参考图像用于无人机（UAV）定位的潜力。这样的基于视觉的定位增强系统至关重要，尤其是在全球定位系统（GPS）的完整性受到质疑的情况下（例如，发生GPS中断，干扰等）。为此，我们提出了一条完整的可训练管道，以在无人机定位的情况下将航空图像定位在预先存储的正马赛克地图中。拟议中的深层架构从航拍图像中提取特征并将其本地化为预先设定的，更大且带有地理标记的图像。这个想法是训练一种深度学习模型，以通过引入半局部约束来找到将共识模式封装在已建立的密集特征对应关系附近的局部共识模式。我们定性和定量评估我们的方法在真实无人机图像上的性能。训练和测试数据是通过不同地区的多次飞行获取的。源代码以及整个数据集（包括所收集图像的注释）已公开。 ^{1个 1个}

https://github.com/m-hamza-mughal/Aerial-Template-Matching。

据我们所知，这种数据集是新颖的，它是首个此类数据集，该数据集由在不同时间在巴基斯坦三个不同地区采集的2052个高分辨率航拍图像组成，总面积约2 km $ ^ 2 $ 。