pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons

TL;DR

This paper introduces pyrtklib, a Python binding for RTKLIB, enabling seamless integration of deep learning with GNSS positioning to improve accuracy in urban environments.

Contribution

It presents pyrtklib, a novel Python interface for RTKLIB, and a deep learning subsystem that enhances GNSS positioning accuracy in urban canyons.

Findings

Enhanced positioning accuracy demonstrated in urban canyon scenarios

Seamless integration of deep learning with GNSS algorithms in Python

Facilitated rapid prototyping of AI-assisted GNSS solutions

Abstract

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.