PACE：用于PairUAV相对定位的极轴条件估计

中文摘要 AI 辅助

PairUAV相对定位将两个无人机图像映射到极坐标导航指令。虽然航向和距离共享相同的成对姿态上下文，但将它们视为齐次坐标会使两个输出使用相同的解码器证据和优化状态。可控读出探测器显示出不同的结构：两个轴有利于不同的解码器深度组合，它们的最佳检查点在验证轨迹的80.8%上不一致，并且距离误差呈现出明显的高误差尾部。我们引入了极轴条件估计方法，它保留了共享的Reloc3r风格的成对表示，同时分配特定于轴的读出接口。航向使用中/后期关系证据，而距离仍附着于直接的后期度量路径。在官方隐藏测试中，最强的已发布原始预测器得分为0.002460；互补的PAAER预测器得分为0.002514，角度误差略低。与学习估计分开报告的确定性挑战包装产生最终得分为0.001874。代码、检查点、预测和重建工具可在该https URL获得。

英文摘要

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.

发表机构

• FST and ICI, University of Macau（澳门大学科技学院和创新与合作研究所）

机构由 AI 辅助整理，请以论文原文为准。