Unmanned Aerial Vehicle (UAV) localization capability is critical in a Global
Navigation Satellite System (GNSS) denial environment. The aim of this paper is
to investigate the problem of locating the UAV itself through a purely visual
approach. This task mainly refers to: matching the corresponding geo-tagged
satellite images through the images acquired by the camera when the UAV does
not acquire GNSS signals, where the satellite images are the bridge between the
UAV images and the location information. However, the sampling points of
previous cross-view datasets based on UAVs are discrete in spatial distribution
and the inter-class relationships are not established. In the actual process of
UAV-localization, the inter-class feature similarity of the proximity position
distribution should be small due to the continuity of UAV movement in space. In
view of this, this paper has reformulated an intensive dataset for UAV
positioning tasks, which is named DenseUAV, aiming to solve the problems caused
by spatial distance and scale transformation in practical application
scenarios, so as to achieve high-precision UAV-localization in GNSS denial
environment. In addition, a new continuum-type evaluation metric named SDM is
designed to evaluate the accuracy of model matching by exploiting the continuum
of UAVs in space. Specifically, with the ideas of siamese networks and metric
learning, a transformer-based baseline was constructed to enhance the capture
of spatially subtle features. Ultimately, a neighbor-search post-processing
strategy was proposed to solve the problem of large distance localisation bias.

该研究通过视觉方法探究无人机在全球导航卫星系统拒绝服务环境下如何准确定位自身，为此构建了基于转换器的基线并提出了新的连续型评估指标 SDM，以解决现实应用场景中空间距离和尺度变换所带来的问题。同时，利用孪生网络和度量学习构建，设计邻居搜索后处理策略解决大距离定位偏差问题。