FoundLoc：基于视觉的野外机载空中定位
FoundLoc: Vision-based Onboard Aerial Localization in the Wild
AI总结：
本文提出FoundLoc，一种基于视觉的无人机机载定位流水线，结合VIO与基础模型驱动的VPR，仅依赖下视相机、IMU和卫星图像，在无GNSS且不假设初始位姿的野外环境中实现鲁棒定位，平均误差20米内，最小误差低于1米。
摘要：
无人飞行器（UAV）实现自主、远距离飞行必须具备鲁棒且精确的定位能力。现有方法要么严重依赖GNSS，要么因相机图像与参考图像之间的外观差异和风格不相似而在基于视觉的定位中面临局限，要么在已知初始位姿的假设下运行。本文开发了一种GNSS拒止环境下的UAV定位方法，利用视觉惯性里程计（VIO）和使用基础模型的视觉位置识别（VPR）。本文提出了一种新颖的基于视觉的流水线，仅使用向下相机、惯性测量单元（IMU）和现有卫星图像，即可在各种环境和条件下实现鲁棒、准确的定位。在环境外观发生剧烈变化且不假设飞行器初始位姿的真实条件下，系统平均定位精度在20米范围内，最小误差低于1米。该方法被证明有效且鲁棒，满足了GNSS拒止环境下可靠UAV定位的关键需求，同时计算效率高，足以部署在资源受限的平台上。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
