🤖 AI Summary
This work addresses the challenge of robust drone localization in GNSS-denied environments, where visual-inertial odometry suffers from cumulative drift and appearance-based methods are sensitive to seasonal changes, lighting variations, viewpoint shifts, and map obsolescence. To overcome these limitations, the authors propose a semantic map localization framework that integrates structured semantic geometry with a stability-aware mechanism, leveraging persistent environmental structures such as roads and buildings. The approach employs semantic grid alignment, relational graph matching, geospatial saliency evaluation, and a ternary observation model (positive, contradictory, or unknown), complemented by an integrity-aware fuzzy rejection strategy. Experimental results demonstrate a Recall@1 of 94.5–95.5% across 220 cross-view queries, substantially outperforming global semantic descriptors (58.6%) and confirming the method’s robustness to perturbations including rotation, scaling, and occlusion.
📝 Abstract
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.