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Aerial-DEM geolocalization for GPS-denied UAS navigation

机译：GPS拒绝式UAS导航的Aero-DEM地理定位

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摘要

Accelerated by the proliferation of small, affordable, and lightweight electronically scanning radar systems as well as advances in Unmanned Aircraft System (UAS) technology, Geo-Registered Radar Returns data are becoming an incredible source for geolocalization in GPS-denied UAS navigation. Most existing approaches match aerial images to pre-stored Digital Elevation Models (DEMs) through 3D terrain reconstruction or GPU-based terrain rendering techniques. However, these reconstruction or rendering processes are themselves error-prone and time-consuming, which further decrease UAS navigation accuracy. In this work, we propose a novel geolocalization approach by directly matching aerial images to DEMs. Inspired by success of deep learning in face recognition/verification, we develop a triplet-ranking network to embed aerial images and DEMs into the same low-dimensional feature space, where matching Aerial-DEM are near one another and mismatched Aerial-DEM are far apart. To create large-scale training dataset, we design an efficient terrain generation approach using per-pixel displacement mapping technique. This approach augments aerial datasets by simulating visual appearances of terrain under different lighting conditions. Experiments are conducted to show the effectiveness of our deep network in finding matches between aerial images and DEMs.
机译：小型，价格合理，轻便的电子扫描雷达系统的普及以及无人飞机系统（UAS）技术的发展加速了地理登记的雷达回程数据，这些数据已成为GPS受限的UAS导航中不可思议的地理定位来源。现有的大多数方法都是通过3D地形重建或基于GPU的地形渲染技术将航拍图像与预存的数字高程模型（DEM）匹配。但是，这些重构或渲染过程本身容易出错且耗时，这进一步降低了UAS导航的准确性。在这项工作中，我们提出了一种通过直接将航拍图像与DEM相匹配的新颖的地理定位方法。受到深度学习在面部识别/验证中取得成功的启发，我们开发了一个三元组排列网络，将航空图像和DEM嵌入到同一低维特征空间中，在该低维特征空间中，匹配的Aero-DEM彼此接近，而失配的Aero-DEM则很远分开。为了创建大规模训练数据集，我们使用每像素位移映射技术设计了一种有效的地形生成方法。该方法通过模拟不同光照条件下地形的视觉外观来增强航空数据集。进行实验以证明我们的深度网络在寻找航空影像和DEM之间的匹配方面的有效性。

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