Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization

2026-03-03

AI Summary

This work proposes a real-time tightly coupled GNSS-IMU fusion method based on factor graph optimization to address the challenges of high-precision positioning in dense urban environments, where signal blockage, multipath effects, and rapidly changing satellite geometry severely degrade performance. By integrating incremental optimization with fixed-lag marginalization within a factor graph framework, the approach achieves real-time causal inference while maintaining high estimation accuracy—overcoming the limitations of conventional offline processing. To the best of our knowledge, this is the first implementation of a factor graph-based optimizer in a real-time tightly coupled GNSS-IMU system. Experimental results on the highly challenging UrbanNav urban dataset demonstrate that the proposed method outperforms existing state-of-the-art approaches in terms of real-time capability, robustness, and positioning accuracy.

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
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Reliable positioning in dense urban environments remains challenging due to frequent GNSS signal blockage, multipath, and rapidly varying satellite geometry. While factor graph optimization (FGO)-based GNSS-IMU fusion has demonstrated strong robustness and accuracy, most formulations remain offline. In this work, we present a real-time tightly coupled GNSS-IMU FGO method that enables causal state estimation via incremental optimization with fixed-lag marginalization, and we evaluate its performance in a highly urbanized GNSS-degraded environment using the UrbanNav dataset.

Problem

• GNSS
• urban environment
• signal blockage
• multipath
• satellite geometry

Innovation

• Factor Graph Optimization
• Real-time GNSS-IMU Integration
• Fixed-lag Marginalization
• Tightly Coupled Fusion
• Urban Positioning