提出可感知距离的尺度恢复方法，提升无人机在无GPS时的定位精度。
RASR: Range-Aware Scale Recovery for Metric UAV Navigation
- 用冻结的MASt3R模型提取成对几何信息，生成紧凑描述符
- 在PairUAV挑战中总误差降至0.003189，优于全局尺度校准
- 适合需要高精度距离估计的无人机导航任务
在无全球导航卫星系统（GNSS）环境下，图像目标无人机导航的核心挑战是估算当前视图与目标视图之间的度量距离和航向。密集成对几何模型能捕捉相对场景结构，但缺乏校准的度量尺度，无法直接提供可靠的导航距离估计。尽管全局尺度校准可修正主导尺度偏差，但剩余误差仍随距离系统性变化。本文提出范围感知尺度恢复（RASR），通过范围感知残差校正补充全局尺度校准。RASR将冻结的匹配与立体3D重建（MASt3R）主干提取的成对几何编码为紧凑描述符，并将尺度恢复核心与任务特定命令校准分离。在Multimedia 2026 PairUAV挑战赛官方在线评估中，RASR总误差达0.003189，低于仅使用全局尺度校准的结果。结果表明，范围感知残差校正显著提升了度量距离估计性能。代码与材料见https://github.com/lht-research/rasr-pairuav。
原文摘要 · Abstract (English)
A central challenge in image-goal UAV navigation under Global Navigation Satellite System (GNSS) denial is estimating metric distance and heading between current and goal views. Dense pairwise geometry models capture relative scene structure, but without a calibrated metric scale, they cannot directly provide reliable distance estimates for navigation. Although global scale calibration corrects the dominant scale bias, the remaining errors vary systematically with distance. In this paper, Range-Aware Scale Recovery (RASR) is proposed, which complements global scale calibration with range-aware residual correction. RASR encodes pairwise geometry extracted by a frozen Matching And Stereo 3D Reconstruction (MASt3R) backbone as a compact descriptor and separates the scale-recovery core from task-specific command calibration. On the official online evaluation of the UAVs in Multimedia 2026 PairUAV challenge, RASR achieved a total error of 0.003189, achieving a lower total error than global scale calibration alone. The results demonstrate that range-aware residual correction improves metric distance estimation beyond global scale calibration. Code and materials are available at https://github.com/lht-research/rasr-pairuav.
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