SASGeo: Stability-Aware Semantic Map Localization for GNSS-Denied UAVs -- A Framework and Synthetic Proof of Concept
arXiv:2607.07737v1 Announce Type: cross 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
The increasing proliferation and sophistication of autonomous aerial systems, particularly in contested environments, necessitates robust navigation solutions independent of traditional GPS/GNSS vulnerabilities.
This research addresses a critical vulnerability in autonomous systems, enabling more reliable and resilient operations for UAVs in environments where satellite navigation is degraded or denied, with significant implications for defense and critical infrastructure.
Autonomous UAVs can achieve more accurate and stable localization without relying solely on satellite signals, improving operational endurance and reducing susceptibility to jamming or spoofing.
- · Defence contractors
- · Autonomous drone manufacturers
- · Logistics and reconnaissance sectors
- · Geospatial intelligence providers
- · Adversaries relying on GNSS denial tactics
- · Systems solely dependent on GNSS for navigation
UAVs gain enhanced operational resilience and autonomy in GNSS-denied or contested zones.
This capability allows for expanded military and commercial applications of UAVs in previously inaccessible or high-risk areas.
The increased independence from satellite navigation could influence future doctrine for autonomous warfare and infrastructure inspection.
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Read at arXiv cs.LG