灯光熄灭：一种利用热成像和语义3D地图的夜间无人机定位框架
Lights Out: A Nighttime UAV Localization Framework Using Thermal Imagery and Semantic 3D Maps
AI总结：
本文提出了一种基于语义重投影的夜间无人机定位方法，通过将分割后的热成像与白天RGB数据构建的语义3D地图对齐，实现GNSS拒止条件下的可靠定位，实验表明语义边缘证据的可用性对定位性能有显著影响。
摘要：
可靠的无人机在GNSS拒止夜间条件下备用定位仍是一个开放性挑战，由于白天RGB地图与夜间热成像之间存在严重的模态差距。本文提出了一种语义重投影框架，通过将分割后的热成像与由白天RGB数据构建的全局参考语义标记3D地图对齐，实现基于地图的夜间无人机定位。而非依赖于基于外观的对应关系，定位是在共享的语义域中进行，并通过具有混淆意识加权的对称双向重投影目标解决，以提高在分割不确定性下的鲁棒性。该方法在6.5公里的夜间真实世界无人机飞行轨迹上进行了离线评估。相对于RTK GNSS地面真实值，系统实现了偏置校正后的RMSE2D为2.18米，中位数RMSE2D为1.52米。结果表明，定位性能与语义边缘证据的可用性有很强的相关性，并且大误差事件集中在语义模糊区域而非均匀分布。这些发现表明，语义重投影为仅使用热成像实现全局参考的夜间无人机定位提供了有前途的路径。
英文摘要：
Reliable backup localization for unmanned aerial vehicles (UAVs) operating in GNSS-denied nighttime conditions remains an open challenge due to the severe modality gap between daytime RGB maps and nighttime thermal imagery. This work presents a semantic reprojection framework for map-relative nighttime UAV localization by aligning segmented thermal observations with a globally referenced, semantically labeled 3D map constructed from daytime RGB data. Rather than relying on appearance-based correspondence, localization is formulated in a shared semantic domain and solved via a symmetric bidirectional reprojection objective with confusion-aware weighting to improve robustness under segmentation uncertainty. The approach is evaluated offline across 6.5 km of nighttime, real-world UAV flight trajectories in urban and semi-structured environments. Relative to RTK GNSS ground truth, the system achieves a bias-corrected RMSE2D of 2.18 m and a median RMSE2D of 1.52 m. Results show that localization performance is strongly correlated with the availability of semantic edge evidence and that large-error events are spatially localized to semantically ambiguous areas rather than uniformly distributed. These findings indicate that semantic reprojection offers a promising pathway toward globally referenced nighttime UAV localization using thermal imagery alone.
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