Cross-view geolocalization, a supplement or replacement for GPS, localizes an agent within a search area by matching images taken from a ground-view camera to overhead images taken from satellites or aircraft. Although the viewpoint disparity between ground and overhead images makes cross-view geolocalization challenging, significant progress has been made assuming that the ground agent has access to a panoramic camera. For example, our prior work (WAG) introduced changes in search area discretization, training loss, and particle filter weighting that enabled city-scale panoramic cross-view geolocalization. However, panoramic cameras are not widely used in existing robotic platforms due to their complexity and cost. Non-panoramic cross-view geolocalization is more applicable for robotics, but is also more challenging. This paper presents Restricted FOV Wide-Area Geolocalization (ReWAG), a cross-view geolocalization approach that generalizes WAG for use with standard, non-panoramic ground cameras by creating pose-aware embeddings and providing a strategy to incorporate particle pose into the Siamese network. ReWAG is a neural network and particle filter system that is able to globally localize a mobile agent in a GPS-denied environment with only odometry and a 90 degree FOV camera, achieving similar localization accuracy as what WAG achieved with a panoramic camera and improving localization accuracy by a factor of 100 compared to a baseline vision transformer (ViT) approach. A video highlight that demonstrates ReWAG's convergence on a test path of several dozen kilometers is available at https://youtu.be/U_OBQrt8qCE.

翻译：跨视角地理定位作为GPS的补充或替代方案，通过将地面视角相机拍摄的图像与卫星或航空拍摄的俯视图像进行匹配，在搜索区域内定位智能体。尽管地面与俯视图像间的视角差异使跨视角定位极具挑战性，但假设地面智能体配备全景相机的情况下已取得显著进展。例如，我们先前的工作（WAG）通过引入搜索区域离散化、训练损失和粒子滤波权重的改进，实现了城市规模的全景跨视角地理定位。然而，由于复杂性和成本问题，全景相机在现有机器人平台中尚未普及。非全景跨视角地理定位虽更具机器人应用价值，但挑战性也更高。本文提出受限视场大范围地理定位方法（ReWAG），这是一种通过创建姿态感知嵌入，并将粒子姿态融入孪生网络的通用化WAG方法，适用于标准非全景地面相机。ReWAG构建了神经网络与粒子滤波系统，在仅依靠里程计和90度视场相机的无GPS环境中，能够实现移动智能体的全局定位，其定位精度与全景相机WAG相当，相比基准视觉Transformer方法（ViT）提升了两个数量级。展示ReWAG在数十公里测试路径上收敛效果的视频见https://youtu.be/U_OBQrt8qCE。