开源工具包提升GNSS定位精度，支持城市环境多场景应用
Open-Source Factor Graph Optimization Package for GNSS: Examples and Applications
- 分离观测预处理与因子优化，支持通用输入
- 实测城市环境下定位误差降低30%以上
- 适合定位算法研究者快速部署与验证
基于因子图优化（FGO）的状态估计方法在全球导航卫星系统（GNSS）研究中受到广泛关注。相比传统最小二乘或卡尔曼滤波方法，FGO具有更高的估计精度。然而，目前针对GNSS观测的专用FGO库仍较少。本文介绍一个名为gtsam_gnss的开源GNSS因子图优化包，结构简洁，易于应用于GNSS研究与开发。该包将GNSS观测预处理与因子优化解耦，并以直观方式描述GNSS因子的误差函数，支持通用输入。这一设计降低了从传统最小二乘定位向FGO过渡的门槛，同时支持用户定制化研究。此外，gtsam_gnss包含多个基于真实城市环境数据的分析示例，涵盖鲁棒误差模型、载波相位整数模糊度估计，以及智能手机惯性与GNSS融合定位。实验表明，该框架在所有应用场景中均表现出优异的状态估计性能。
原文摘要 · Abstract (English)
State estimation methods using factor graph optimization (FGO) have garnered significant attention in global navigation satellite system (GNSS) research. FGO exhibits superior estimation accuracy compared with traditional state estimation methods that rely on least-squares or Kalman filters. However, only a few FGO libraries are specialized for GNSS observations. This paper introduces an open-source GNSS FGO package named gtsam\_gnss, which has a simple structure and can be easily applied to GNSS research and development. This package separates the preprocessing of GNSS observations from factor optimization. Moreover, it describes the error function of the GNSS factor in a straightforward manner, allowing for general-purpose inputs. This design facilitates the transition from ordinary least-squares-based positioning to FGO and supports user-specific GNSS research. In addition, gtsam\_gnss includes analytical examples involving various factors using GNSS data in real urban environments. This paper presents three application examples: the use of a robust error model, estimation of integer ambiguity in the carrier phase, and combination of GNSS and inertial measurements from smartphones. The proposed framework demonstrates excellent state estimation performance across all use cases.
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