GeoVINS: Geographic-Visual-Inertial Navigation System for Large-Scale Drift-Free Aerial State Estimation

This article introduces GeoVINS, a vision-based navigation framework designed for large-scale global state estimation. By utilizing geographic information from satellite orthoimagery, GeoVINS tackles scale ambiguity and accumulative drift problems inherent in standard visual-inertial simultaneous localization and mapping (VI-SLAM) systems, and provides accurate, robust, and real-time global localization. In particular, to address the challenge of memory explosion issue for large-scale localization, we propose a novel aerial ‘classify-then-retrieve’ aerial VPR approach, where geographic locations can be efficiently identified and memory usage can be reduced by 3 to 4 orders of magnitude compared to classical retrieval-based approaches. In addition, a hierarchical geographic data association scheme, enhanced by state-of-the-art deep learning-based feature matching, guarantees high efficiency and robustness against variations in appearance and viewpoint. Relying on the obtained three-dimensional (3D) geographic information, GeoVINS achieves efficient global state initialization and precise motion tracking. To address GPU limitations in embedded devices, a collaborative CPU-GPU utilization approach is proposed, seamlessly integrating asynchronous global information to eliminate accumulative errors. Relying solely on satellite imagery and without requiring any other prior information, GeoVINS achieves rapid place recognition in unseen environments of city-scale (e.g., 2500${\mathrm{km}}^{2}$) on an embedded computing device with an inference time of 43 ms, and performs state estimation at a frequency of 25 Hz. The system enables autonomous UAV navigation as an alternative to GNSS.