Open-Source Factor Graph Optimization Package for GNSS: Examples and Applications

2025-02-12

AI Summary

Conventional GNSS state estimation methods—such as least squares and Kalman filtering—suffer from limited accuracy, while dedicated factor graph optimization (FGO) tools for GNSS remain scarce. Method: This paper introduces *gtsam_gnss*, a lightweight, open-source GNSS-specific FGO toolkit built upon GTSAM. It features a novel modular factor graph framework that decouples observation preprocessing from nonlinear optimization, enabling robust error modeling, carrier-phase integer ambiguity constraints, and tight GNSS/IMU integration. Crucially, it implements analytical Jacobian computation and efficient integer-constrained optimization. Contribution/Results: Evaluated in real-world urban environments, *gtsam_gnss* achieves 30–50% improvement in positioning accuracy over baseline methods across three representative localization tasks, demonstrating superior performance and practical viability.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Security and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Develops open-source GNSS factor graph optimization package
• Enhances GNSS state estimation accuracy and flexibility
• Supports diverse GNSS research and application scenarios

Innovation

• Open-source GNSS FGO package
• Separates preprocessing from optimization
• Supports robust error modeling