pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons
This work addresses a technological gap for developers in intelligent transportation systems by providing a tool to integrate deep learning with GNSS, though it is incremental as it builds on existing RTKLIB functionalities.
The paper tackles the integration challenge between traditional GNSS algorithms and deep learning tools by introducing pyrtklib, a Python binding for RTKLIB, which enables seamless prototyping and implementation of deep learning-aided GNSS algorithms, resulting in enhanced positioning accuracy.
Artificial intelligence (AI) is revolutionizing numerous fields, with increasing applications in Global Navigation Satellite Systems (GNSS) positioning algorithms in intelligent transportation systems (ITS) via deep learning. However, a significant technological disparity exists as traditional GNSS algorithms are often developed in Fortran or C, contrasting with the Python-based implementation prevalent in deep learning tools. To address this discrepancy, this paper introduces pyrtklib, a Python binding for the widely utilized open-source GNSS tool, RTKLIB. This binding makes all RTKLIB functionalities accessible in Python, facilitating seamless integration. Moreover, we present a deep learning subsystem under pyrtklib, which is a novel deep learning framework that leverages pyrtklib to accurately predict weights and biases within the GNSS positioning process. The use of pyrtklib enables developers to easily and quickly prototype and implement deep learning-aided GNSS algorithms, showcasing its potential to enhance positioning accuracy significantly.