用Python封装RTKLIB，让深度学习直接优化城市峡谷中的定位精度
pyrtklib: An open-source package for tightly coupled deep learning and GNSS integration for positioning in urban canyons
- 基于pyrtklib构建深度学习框架，间接训练伪距偏差与权重
- 在城市峡谷场景中定位精度超越goGPS和RTKLIB
- 适合智能交通系统开发者快速实现深度学习融合的定位算法
人工智能正推动智能交通系统中全球导航卫星系统（GNSS）定位算法的革新，但传统GNSS算法多用Fortran或C编写，与主流深度学习工具的Python环境存在技术鸿沟。为此，本文提出pyrtklib——一个面向广受欢迎的开源GNSS工具RTKLIB的Python绑定，使所有RTKLIB功能可在Python中调用，实现无缝集成。此外，我们在pyrtklib下构建了新型深度学习子系统，利用其精准预测GNSS定位过程中的权重与偏差。该框架使开发者能快速原型化并实现深度学习增强的GNSS算法。对比分析表明，所提出的间接训练方法在城市峡谷场景中显著优于goGPS和RTKLIB，提升了定位精度。
原文摘要 · Abstract (English)
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.
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