区分航向与距离的解码机制，提升无人机相对定位精度
PACE: Polar Axis-Conditioned Estimation for PairUAV Relative Localization
- 航向用中后期关系特征，距离保留直接度量路径
- 测试集上距离误差有明显高错尾部，航向与距离最佳模型仅80.8%一致
- 新方法在官方测试集达0.001874，优于单一模型
PairUAV相对定位将两架无人机图像映射为极坐标导航指令。尽管航向与距离共享同一对位姿上下文，但将其视为同质坐标会迫使两者使用相同的解码器证据和优化状态。控制读出探查显示：两个轴偏好不同的解码器深度组合，其最佳检查点在验证轨迹上仅有80.8%一致，且距离误差呈现显著的高错误尾部。本文提出极轴条件估计（PACE），在保留类似Reloc3r的配对表示基础上，为不同轴分配独立的读出接口：航向采用中/晚期关系特征，距离则维持直接晚期度量路径。官方隐藏测试中，最强原始预测器得分为0.002460；互补的PAAER预测器得分为0.002514，角度误差略低。确定性挑战打包结果单独报告，最终得分0.001874。代码、检查点、预测与重建工具已开源：https://github.com/zerong7777-boop/PairUAV-PACE。
原文摘要 · Abstract (English)
PairUAV relative localization maps two UAV images to a polar navigation command. Although heading and range share the same pairwise pose context, treating them as homogeneous coordinates forces both outputs to use the same decoder evidence and optimization state. Controlled readout probes reveal a different structure: the two axes favor different decoder-depth combinations, their best checkpoints disagree on 80.8% of a validation trajectory, and range errors exhibit a distinct high-error tail. We introduce method, Polar Axis-Conditioned Estimation, which retains a shared Reloc3r-style pair representation while assigning axis-specific readout interfaces. Heading uses mid/late relational evidence, whereas range remains attached to a direct late metric path. On the official hidden test, the strongest released raw predictor scores 0.002460; the complementary PAAER predictor scores 0.002514 with a slightly lower angle error. Deterministic challenge packaging, reported separately from learned estimation, yields the final score of 0.001874. Code, checkpoints, predictions, and reconstruction tools are available at https://github.com/zerong7777-boop/PairUAV-PACE.
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