AI 中文总结
研究针对高空无人机视觉地理定位问题，提出NGPS框架，通过深度卫星图像匹配及多速率传感器融合实现无GPS绝对定位，结合多种技术，经实验在五个飞行序列上取得良好效果，相比单目VIO有显著提升，且能实时运行并部分开源。
AI 中文摘要
我们提出了NGPS（下一代定位系统），这是一种用于高空无人机的视觉地理定位框架，通过将向下拍摄的图像与具有深度特征的地理参考卫星图像进行匹配，提供无GPS的绝对定位。该系统结合了：（1）自适应置信加权无迹卡尔曼滤波融合，其协方差由RANSAC内点率、重投影误差和匹配置信度调制；（2）速度预测内核提取，利用视觉惯性里程计（VIO）速度预测卫星搜索区域；（3）异步多速率时间优先级队列，按时间顺序交错绝对位置（1-2Hz）、VIO（10-20Hz）和惯性测量单元（IMU，100-200Hz）更新。由NGPS校正锚定的VINS位姿图优化得到的全局优化位姿，进一步实现了实时2.5D地理参考正射镶嵌重建。在五个飞行序列（海拔6~150米）上，NGPS实现了2.94米的位置均方根误差（RMSE），在海拔150米、速度2米/秒时，最坏情况下的绝对轨迹误差（ATE）为6.04米，比独立单目VIO提高了3.5倍。该系统在NVIDIA Jetson Orin NX上实时运行。部分实现已在该https网址开源。
英文摘要
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.
Comments9 pages (8-page paper + IEEE copyright/citation cover page), 6 figures. Accepted to IEEE/RSJ IROS 2026
