AI 中文总结
AeroMap3D是用于无GNSS导航的单目无人机6自由度定位系统，针对地图参考空中定位挑战，引入轻量级适配器，利用地图注释去除不可靠匹配，结合延迟状态EKF融合测量与先验，实现高精度定位，多地轨迹定位误差小。
AI 中文摘要
我们提出了AeroMap3D，一种单目6自由度无人机定位系统，用于在无GNSS导航时将机载图像锚定到视觉、几何和语义地图先验上。它解决了地图参考空中定位中的两个基本挑战：无人机图像与卫星地图之间的跨视图差异，以及裸地数字高程模型与城市场景之间的结构不一致。首先引入轻量级适配器，使在互联网规模通用数据上预训练的密集匹配器能可靠地进行无人机到地图的配准。其次，在RANSAC-PnP姿态估计前利用OpenStreetMap注释去除语义不可靠匹配，将二维对应关系提升到DEM地形上。最后使用延迟状态EKF融合基于地图的姿态测量和相对运动先验进行连续轨迹估计。在未经无人机-Terra3D再训练或调整的情况下，AeroMap3D能在50米内定位奥斯汀八个地点的所有轨迹，在55公里飞行中平均3D误差为5 .88米。
英文摘要
We present AeroMap3D, a monocular 6-DoF UAV localization system that anchors onboard imagery to visual, geometric, and semantic map priors for GNSS-denied navigation. AeroMap3D addresses two fundamental challenges in map-referenced aerial localization: the cross-view discrepancy between UAV imagery and satellite maps, and the structural inconsistency between bare-earth digital elevation models (DEMs) and urban scenes. First, we introduce a lightweight adapter that enables a dense matcher pretrained on internet-scale generic data to perform reliable UAV-to-map registration without finetuning. By estimating the scale ratio and yaw offset between the UAV image and map tile, the adapter removes the dominant geometric misalignment induced by altitude, camera field of view, and heading before dense correspondence estimation. Second, AeroMap3D lifts 2D UAV-map correspondences onto DEM terrain while using OpenStreetMap annotations to reject semantically unreliable matches before RANSAC-PnP pose estimation, thereby reducing errors caused by unmodeled building heights and off-nadir structures. Delayed map-based pose measurements are further fused with relative-motion priors using a delayed-state EKF for continuous trajectory estimation. Without UAV-Terra3D retraining or tuning, AeroMap3D localizes all trajectories across eight Austin sites within 50 m and achieves 5.88 m mean 3D error over 55 km of flight.
