无需GPS，通过卫星图像匹配实现无人机高精度定位与三维重建。
NGPS: GPS-Denied Aerial Geo-Localization and 2.5D Reconstruction via Deep Satellite Image Matching and Multi-Rate Sensor Fusion
- 用深度特征匹配航拍图与卫星图，结合自适应卡尔曼滤波提升定位精度。
- 在60-150米高度下定位均方根误差2.94米，最差情况仅6.04米，性能比纯视觉系统提升3.5倍。
- 适合高海拔无人机自主导航与实时地理测绘，代码已开源。
我们提出NGPS（下一代定位系统），一种面向高空无人机的视觉地理定位框架，可在无GPS条件下通过匹配向下视角图像与地理参考卫星影像实现绝对定位。系统融合三项核心技术：(1) 自适应置信加权的无迹卡尔曼滤波，其协方差由RANSAC内点率、重投影误差和匹配置信度动态调节；(2) 基于视觉惯性里程计（VIO）速度预测卫星搜索区域；(3) 异步多速率时间优先队列，按时间顺序交错处理1-2 Hz的绝对位置、10-20 Hz的VIO和100-200 Hz的IMU更新。基于全局优化的VINS位姿图，以NGPS校正为锚点，进一步实现实时2.5D地理参考正射影像重建。在五组飞行序列（60-150米离地高度）中，NGPS定位均方根误差为2.94米，最坏情况下的绝对轨迹误差（ATE）为6.04米（150米高度、2米/秒速度），相较独立单目VIO提升3.5倍。系统在NVIDIA Jetson Orin NX上实时运行。部分代码已开源：https://github.com/snktshrma/ngps_flight。
原文摘要 · Abstract (English)
We present NGPS (Next-Generation Positioning System), a visual geo-localization framework for high-altitude UAVs that provides GPS-free absolute positioning by matching down-facing images to georeferenced satellite imagery with deep features. The system combines (1) adaptive confidence-weighted UKF fusion, where NGPS covariance is modulated by RANSAC inlier ratio, reprojection error, and match confidence; (2) velocity-predictive kernel extraction, using VIO velocity to predict the satellite search region; and (3) an asynchronous multi-rate temporal priority queue that interleaves absolute position (1-2 Hz), VIO (10-20 Hz), and IMU (100-200 Hz) updates in chronological order. Globally optimized poses from VINS pose-graph optimization, anchored by NGPS corrections, further enable real-time 2.5D georeferenced orthomosaic reconstruction. On five flight sequences (60-150 m AGL), NGPS achieves 2.94 m position RMSE, with worst-case ATE 6.04 m at 150 m AGL and 2 m/s, yielding a 3.5x improvement over standalone monocular VIO. The system runs in real time on an NVIDIA Jetson Orin NX. Part of the implementation is open-sourced at https://github.com/snktshrma/ngps_flight.
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