通过将机载相机图像与正射影像进行视觉匹配实现无人机的 GNSS 拒止环境地理定位
GNSS-denied geolocalization of UAVs by visual matching of onboard camera images with orthophotos
AI总结：
本文提出一种 Monte-Carlo 地理定位方法，利用惯性测量、任意朝向相机和正射影像地图，通过图像正射校正与匹配评分，在 GNSS 拒止环境中实现无人机全局定位。
摘要：
低成本无人机（UAV）的定位通常依赖全球导航卫星系统（GNSS）。GNSS 既容易受到无线电信号自然中断的影响，也容易受到敌方故意干扰和欺骗的影响。为小型无人机提供不依赖 GNSS 的地理参考定位的一种典型方法，是配备一个朝下的相机，并将相机图像与地图进行匹配。朝下的相机会增加无人机平台的成本、尺寸和重量，而且其朝向限制了它用于其他目的的可用性。在本工作中，我们提出一种 Monte-Carlo 定位方法，用于无人机的地理参考定位；该方法不需要基础设施，只使用惯性测量、一个朝向任意方向的相机以及一幅正射影像地图。我们基于环境的局部平面性假设对无人机图像进行正射校正，从而放宽了对相机朝下指向的要求。我们提出了一种用于衡量正射校正后无人机图像与地图匹配得分优劣的度量。我们证明，该系统能够在对初始化和地图分辨率要求适中的情况下，对无人机进行全局定位。
英文摘要：
Localization of low-cost Unmanned Aerial Vehicles (UAVs) often relies on Global Navigation Satellite Systems (GNSS). GNSS are susceptible to both natural disruptions to radio signal and intentional jamming and spoofing by an adversary. A typical way to provide georeferenced localization without GNSS for small UAVs is to have a downward-facing camera and match camera images to a map. The downward-facing camera adds cost, size, and weight to the UAV platform and the orientation limits its usability for other purposes. In this work, we propose a Monte-Carlo localization method for georeferenced localization of an UAV requiring no infrastructure using only inertial measurements, a camera facing an arbitrary direction, and an orthoimage map. We perform orthorectification of the UAV image, relying on a local planarity assumption of the environment, relaxing the requirement of downward-pointing camera. We propose a measure of goodness for the matching score of an orthorectified UAV image and a map. We demonstrate that the system is able to localize globally an UAV with modest requirements for initialization and map resolution.
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。
