A UAV Location Method Based on Aerial-Satellite Image Matching and Factor Graph Optimization in GNSS Denied Aera

Global Navigation Satellite System (GNSS) standalone or GNSS/Inertial Navigation System (INS) integrated apparatus is the essential part for a low-altitude Unmanned Aerial Vehicle (UAV) to achieve reliable location information. However, GNSS is fragile and easily attacked, it is critical to develop other absolute location methods for UAV under GNSS-denied environments. In this paper, an aerial-satellite images matching based visual location system (AS-VPS) is proposed. Two key modules “front-end visual odometry” and “back-end absolute location” are included in the AS-VPS. A deep learning method “SuperPoint+LightGlue” is utilized in the front-end consecutive aerial images matching for visual odometry and back-end aerial-satellite images matching for absolute location. In view of the characteristic that the back-end absolute location is not continuous, Factor Graph Optimization (FGO) method is then employed to integrate the position results from the front-end and back-end module, final optimized results are generated. In addition, a cooperative AS-VPS system is set up using the FGO method with the inter-agent ranging measurement. In the experiment, real-world flight-testing datasets are collected to assess the performance of the proposed AS-VPS method, the experimental result demonstrate the superior performance of the proposed metho without GNSS.