发表机构
University of Macau; Institute of Artificial Intelligence and Brain Sciences（澳门大学; 人工智能与脑科学研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于VGGT-Ω的SatFix框架，实现无人机从单张或多张倾斜图像在卫星地图中的绝对定位，在University-Metric数据集上取得优于VGGT-Ω基线的定位精度，推理速度快。
AI 中文摘要
我们研究在给定地理参考卫星区域内的无人机绝对度量定位问题，旨在从单张倾斜图像或短多视图片段中恢复连续地图位置与观测航向。现有跨视图地理定位方法从图库中检索最相似的卫星图块，并报告Recall@K指标，但该检索依赖图库采样，无法提供航向估计，且返回图块索引而非连续坐标。我们提出SatFix，这是一个基于VGGT-Ω构建的前馈无人机-卫星定位框架。卫星网格特征作为查询，聚合无人机视觉证据，两个轻量级头回归卫星地图坐标系中的3自由度位姿：连续2D位置与航向。SatFix无需显式3D地图、渲染鸟瞰图、辅助传感器或测试时位姿对齐。单个模型支持单视图与多视图输入，多视图训练期间使用轨迹约束。为进行度量评估，我们引入University-Metric数据集，该数据集的卫星图像在边长比原始University-1652图块长10.7倍（约为地面面积的114倍）的区域重新采集，包含原始无人机航迹的连续位置与航向标签。使用单张无人机视图时，SatFix在50米内定位52.08%的测试帧，在10米内定位17.34%的测试帧，中位位置误差为45.66米，中位航向误差为20.81°；在NVIDIA RTX 4090上，单视图查询推理耗时不足0.1秒。使用9张无人机视图时，中位位置误差降至21.96米，中位航向误差降至8.73°。与微调后的VGGT-Ω基线相比，SatFix将中位位置误差降低34.0%，并将9视图中位航向误差从25.43°降至8.73°。
英文摘要
We study absolute metric UAV localization within a provided geo-referenced satellite region, recovering continuous map position and viewing heading from a single oblique image or a short multi-view clip. Existing cross-view geo-localization methods retrieve the most similar satellite tile from a gallery and report Recall@K, but retrieval depends on gallery sampling, provides no heading estimate, and returns a tile index rather than a continuous coordinate. We propose SatFix, a feed-forward UAV--satellite localization framework built on VGGT-$Ω$. Satellite-grid features act as queries that aggregate UAV visual evidence, and two lightweight heads regress a 3-DoF pose in the satellite-map frame: continuous 2D position and heading. SatFix requires no explicit 3D map, rendered bird's-eye image, auxiliary sensor, or test-time pose alignment. A single model supports both single- and multi-view inputs, with trajectory constraints used during multi-view training. For metric evaluation, we introduce University-Metric, where satellite imagery is re-collected over a region up to 10.7$\times$ longer on a side (about 114$\times$ the ground area) than the original University-1652 tiles, with continuous position and heading labels for the original UAV tours. With one UAV view, SatFix localizes 52.08% of test frames within 50 m and 17.34% within 10 m, with median position and heading errors of 45.66 m and $20.81^\circ$, respectively. Inference takes under 0.1 s per single-view query on an NVIDIA RTX 4090. With nine UAV views, the median position error falls to 21.96 m and the median heading error to $8.73^\circ$. Compared with a fine-tuned VGGT-$Ω$ baseline, SatFix reduces median position error by 34.0% and nine-view median heading error from $25.43^\circ$ to $8.73^\circ$.
