SASGeo：用于全球导航卫星系统受限无人机的稳定性感知语义地图定位——一个框架和合成概念验证
SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs -- A Framework and Synthetic Proof of Concept
AI总结：
研究针对全球导航卫星系统受限无人机的定位问题，提出SASGeo语义地图定位框架，结合多种技术，指定具体模型并进行合成概念验证。在随机检索试验中，全局语义描述符召回率58.6%，空间语义匹配变体为94.5%-95.5%。该框架能在扰动下区分位置，为后续研究提供方向。
摘要：
全球导航卫星系统受限的无人机需要偶尔进行绝对位置定位，以限制视觉惯性里程计的漂移。跨视图图像检索可提供此类定位，但原始外观对季节、光照、视角、地图时效性和传感器模态敏感。我们提出了SASGeo，一种语义地图定位框架，通过道路、建筑物、水道、铁路、十字路口和田间边界等持久结构来表示环境。该方法结合了语义栅格对齐、关系图证据、特征稳定性和地理独特性、明确的正/反/未知观测以及对模糊定位的完整性感知拒绝。与仅提出宽泛架构不同，本文指定了具体的加权和决策模型，并报告了可重复的合成概念验证。在220次随机检索试验中，全局语义描述符的召回率为58.6%，而空间语义匹配变体的召回率为94.5%-95.5%。实验支持语义几何，但各模块的具体优势有待进一步验证。初步实验未验证实际飞行导航，而是表明结构化语义几何可在受控跨视图扰动下区分位置，并确定了后续更具挑战性的混叠、地图老化和拒绝测试。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
