用热成像与语义3D地图实现夜间无人机精准定位
Lights Out: A Nighttime UAV Localization Framework Using Thermal Imagery and Semantic 3D Maps
- 将热成像与白天建的语义3D地图在语义层面匹配定位
- 2.18米均方根误差，1.52米中位数误差，性能稳定
- 适合夜间无卫星信号环境下的无人机导航系统
在无卫星信号的夜间环境中，无人机可靠定位仍面临挑战，因日间可见光地图与夜间热成像之间存在严重模态差异。本文提出一种基于语义重投影的夜间地图相对定位框架，通过将分割后的热成像观测与由日间RGB数据构建的全局参考语义3D地图对齐实现定位。不依赖外观对应关系，而是在共享语义空间中求解，采用双向对称重投影目标函数并引入混淆感知加权，增强分割不确定性下的鲁棒性。在城市及半结构化环境的6.5公里真实飞行轨迹上离线评估，相对于RTK GNSS真值，系统实现偏置校正后2D RMSE为2.18米，中位数2D RMSE为1.52米。结果表明，定位性能与语义边缘证据可用性高度相关，大误差事件集中于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可行路径。
原文摘要 · Abstract (English)
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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