Robust and accurate localization for Unmanned Aerial Vehicles (UAVs) is an essential capability to achieve autonomous, long-range flights. Current methods either rely heavily on GNSS, face limitations in visual-based localization due to appearance variances and stylistic dissimilarities between camera and reference imagery, or operate under the assumption of a known initial pose. In this paper, we developed a GNSS-denied localization approach for UAVs that harnesses both Visual-Inertial Odometry (VIO) and Visual Place Recognition (VPR) using a foundation model. This paper presents a novel vision-based pipeline that works exclusively with a nadir-facing camera, an Inertial Measurement Unit (IMU), and pre-existing satellite imagery for robust, accurate localization in varied environments and conditions. Our system demonstrated average localization accuracy within a $20$-meter range, with a minimum error below $1$ meter, under real-world conditions marked by drastic changes in environmental appearance and with no assumption of the vehicle's initial pose. The method is proven to be effective and robust, addressing the crucial need for reliable UAV localization in GNSS-denied environments, while also being computationally efficient enough to be deployed on resource-constrained platforms.

翻译：无人机的鲁棒与精确定位是实现自主长距离飞行的关键能力。现有方法要么严重依赖全球导航卫星系统（GNSS），要么因相机图像与参考影像之间的外观差异及风格不相似性而在基于视觉的定位中面临局限性，或者需假设已知初始位姿。本文提出了一种无需GNSS的无人机定位方法，该融合了视觉-惯性里程计（VIO）与基于基础模型的视觉地点识别（VPR）。我们创新性地构建了一套纯视觉处理流程，仅需使用下视相机、惯性测量单元（IMU）和预先存在的卫星图像，便能在多变环境与条件下实现鲁棒精准定位。在无飞行器初始位姿假设的真实场景中，即使面对剧烈环境外观变化，本系统仍实现了平均定位精度在20米范围内、最小误差低于1米的性能。该方法有效且鲁棒，解决了GNSS失效环境下无人机可靠定位的关键需求，同时其计算效率足以部署于资源受限平台。