用深度学习提升城市定位精度，融合道路网与卫星信号
Neural Augmented Kalman Filters for Road Network assisted GNSS positioning
- 用时序图神经网络预测道路段和不确定性，动态优化卡尔曼滤波
- 实测显示复杂场景定位误差降低29%（相比纯卫星定位）
- 适合做高精定位的自动驾驶、移动导航系统研发者
全球导航卫星系统（GNSS）提供全球范围内的定位信息，但在密集城市环境中常受多径效应和非视距误差影响而精度下降。道路网络数据可用于缓解此类误差并提升定位精度。以往方法或仅限离线使用，或依赖缺乏灵活性与鲁棒性的卡尔曼滤波启发式规则。本文提出训练时序图神经网络（TGNN）将道路网络信息融入卡尔曼滤波中，使TGNN预测用户所在道路段及其不确定性，用于卡尔曼滤波的测量更新步骤。基于真实GNSS数据和开源道路网络验证，结果表明在挑战性场景下，定位误差比纯GNSS卡尔曼滤波降低29%。据我们所知，这是首个结合深度学习、道路网络数据与GNSS测量实现地球表面用户定位的端到端方法。
原文摘要 · Abstract (English)
The Global Navigation Satellite System (GNSS) provides critical positioning information globally, but its accuracy in dense urban environments is often compromised by multipath and non-line-of-sight errors. Road network data can be used to reduce the impact of these errors and enhance the accuracy of a positioning system. Previous works employing road network data are either limited to offline applications, or rely on Kalman Filter (KF) heuristics with little flexibility and robustness. We instead propose training a Temporal Graph Neural Network (TGNN) to integrate road network information into a KF. The TGNN is designed to predict the correct road segment and its associated uncertainty to be used in the measurement update step of the KF. We validate our approach with real-world GNSS data and open-source road networks, observing a 29% decrease in positioning error for challenging scenarios compared to a GNSS-only KF. To the best of our knowledge, ours is the first deep learning-based approach jointly employing road network data and GNSS measurements to determine the user position on Earth.
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