  Accurate localization is crucial for various applications, including
autonomous vehicles and next-generation wireless networks. However, the
reliability and precision of Global Navigation Satellite Systems (GNSS), such
as the Global Positioning System (GPS), are compromised by multi-path errors
and non-line-of-sight scenarios. This paper presents a novel approach to
enhance GPS accuracy by combining visual data from RGB cameras with wireless
signals captured at millimeter-wave (mmWave) and sub-terahertz (sub-THz)
basestations. We propose a sensing-aided framework for (i) site-specific GPS
data characterization and (ii) GPS position de-noising that utilizes
multi-modal visual and wireless information. Our approach is validated in a
realistic Vehicle-to-Infrastructure (V2I) scenario using a comprehensive
real-world dataset, demonstrating a substantial reduction in localization error
to sub-meter levels. This method represents a significant advancement in
achieving precise localization, particularly beneficial for high-mobility
applications in 5G and beyond networks.
