面向无人机的季节不变、拒止GNSS视觉定位
Season-invariant GNSS-denied visual localization for UAVs
AI总结：
针对GNSS拒止下无人机视觉定位受季节外观与视角差异影响的问题，提出用CNN匹配相机图像与地理参考正射影像，实验证明其精度、收敛速度及真实无人机鲁棒定位能力优于六种参考方法。
摘要：
在没有全球导航卫星系统（GNSS）的情况下进行定位，是无人机（UAV）自主运行中的一项关键功能。在已知地图上进行基于视觉的定位可以成为一种有效解决方案，但它受到两个主要问题困扰：地点会因天气和季节而呈现不同外观，而且无人机相机图像与地图之间的视角差异使匹配变得困难。在本工作中，我们提出一种定位方案，依靠将无人机相机图像与地理参考正射影像进行匹配，并使用一个经过训练的卷积神经网络模型，该模型对相机图像与地图之间显著的季节外观差异（冬季—夏季）具有不变性。我们将该方案的收敛速度和定位精度与六种参考方法进行比较。结果表明，相对于参考方法有重大改进，尤其是在高季节变化条件下。最后，我们展示了该方法成功定位真实无人机的能力，表明所提方法对视角变化具有鲁棒性。
英文摘要：
Localization without Global Navigation Satellite Systems (GNSS) is a critical functionality in autonomous operations of unmanned aerial vehicles (UAVs). Vision-based localization on a known map can be an effective solution, but it is burdened by two main problems: places have different appearance depending on weather and season, and the perspective discrepancy between the UAV camera image and the map make matching hard. In this work, we propose a localization solution relying on matching of UAV camera images to georeferenced orthophotos with a trained convolutional neural network model that is invariant to significant seasonal appearance difference (winter-summer) between the camera image and map. We compare the convergence speed and localization accuracy of our solution to six reference methods. The results show major improvements with respect to reference methods, especially under high seasonal variation. We finally demonstrate the ability of the method to successfully localize a real UAV, showing that the proposed method is robust to perspective changes.
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