RF-Nav: A Robust Fusion-Based GNSS-Visual-Inertial Navigation System

Accurate vehicle navigation plays a critical role in vehicle-to-everything (V2X) applications, including connected transportation systems, intelligent traffic management, and autonomous driving. To address the stringent demands of these scenarios, the integration of the global navigation satellite system (GNSS) with the visual-inertial navigation system (VINS) has emerged as a pivotal advancement. Despite these strides, navigation systems remain susceptible to abnormal data. This data, originating from unpredictable external environments and internal device fallibility, poses a threat of substantial errors and system drift. In this paper, we present RF-Nav, a robust fusion-based GNSS-VINS navigation system with enhanced data processing and dynamic factor correction. The framework innovates with a dual-pronged approach: it first applies adaptive gamma correction with bilateral filtering and contrast-limited adaptive histogram equalization (AGCBF-CLAHE) to refine raw images; then, it deploys a long short-term memory (LSTM) denoising network enhanced with an advanced wavelet threshold for IMU data refinement. This dual enhancement of visual and IMU data integrity is further bolstered by a dynamic factor confidence correction mechanism, rooted in factor graph optimization (FGO), designed to counteract the adverse effects of abnormal data. Extensive experiments on large-scale public and real-field dataset demonstrate that RF-Nav exhibits superior robustness and accuracy in various environments.