提出结构化语义地图定位框架，提升无卫星无人机在复杂环境下的定位稳定性。
SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs -- A Framework and Synthetic Proof of Concept
- 用道路、建筑等持久结构构建语义地图，融合几何与关系信息增强鲁棒性。
- 在220次随机测试中，空间语义匹配召回率高达94.5%-95.5%，显著优于全局描述符的58.6%。
- 适合需要高精度定位的无人机导航系统，尤其在地图变化或遮挡场景下表现突出。
无卫星信号的无人机需定期获取绝对位置以抑制视觉惯性里程计的漂移。跨视角图像检索可提供此类位置修正，但原始外观易受季节、光照、视角、地图时效性和传感器模态影响。本文提出SASGeo框架，通过道路、建筑、水道、铁路、交叉口和田地边界等持久结构表征环境。方法融合语义栅格对齐、关系图证据、特征稳定性、地理独特性，以及显式的正/反/未知观测与完整性感知的模糊定位剔除机制。不同于仅架构描述的工作，本文明确给出权重与决策模型，并报告可复现的合成验证实验。在包含旋转、缩放、部分裁剪、遮挡、模拟地图变更及强语义干扰物的220次随机检索试验中，全局语义描述符召回率@1为58.6%，而空间语义匹配变体达到94.5%-95.5%。95%置信区间显示全局描述符与空间变体间存在显著差异，但空间变体间重叠，支持语义几何的有效性，而非各模块的确定性增益。初步实验未验证真实飞行导航，但证明结构化语义几何可在受控跨视图扰动下区分位置，并指明后续需加强的混淆、地图老化与拒绝测试。
原文摘要 · Abstract (English)
GNSS-denied unmanned aerial vehicles require occasional absolute position fixes to bound the drift of visual-inertial odometry. Cross-view image retrieval can provide such fixes, but raw appearance is sensitive to season, illumination, viewpoint, map age, and sensor modality. We propose \sas, a semantic map-localization framework that represents the environment through persistent structures such as roads, buildings, waterways, railways, intersections, and field boundaries. The method combines semantic raster alignment, relational graph evidence, feature stability and geographic distinctiveness, explicit positive/contradictory/unknown observations, and integrity-aware rejection of ambiguous fixes. Unlike a broad architecture-only proposal, this paper specifies concrete weighting and decision models and reports a reproducible synthetic proof of concept. In 220 randomized retrieval trials with rotation, scale changes, partial crops, occlusion, simulated map changes, and hard semantic decoys, a global semantic descriptor achieved 58.6\% Recall@1, while spatial semantic matching variants achieved 94.5-95.5%. Wilson 95\% intervals separate the global descriptor from the spatial variants but overlap among the spatial variants, so the experiment supports semantic geometry rather than a definitive benefit from each proposed module. The preliminary experiment does not validate real-flight navigation; rather, it demonstrates that structured semantic geometry can discriminate locations under controlled cross-view perturbations and identifies the harder aliasing, map-aging, and rejection tests required next.
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