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

2026-03-03

AI Summary

This work proposes a real-time loosely coupled GNSS/IMU integration method based on factor graph optimization (FGO) to address the significant degradation in positioning accuracy and availability commonly observed in GNSS-challenged environments such as urban canyons. As the first implementation of FGO-based loose coupling in a real-time system, the approach enhances robustness while maintaining computational efficiency. Evaluated on the UrbanNav-HK-MediumUrban-1 dataset, the method demonstrates higher service availability compared to batch-processing FGO, albeit with a slight reduction in accuracy. This trade-off substantially improves practical deployability in real-world scenarios, systematically revealing the interplay among positioning accuracy, system availability, and computational overhead.

Technology Category

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

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• 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

Abstract

Accurate positioning, navigation, and timing (PNT) is fundamental to the operation of modern technologies and a key enabler of autonomous systems. A very important component of PNT is the Global Navigation Satellite System (GNSS) which ensures outdoor positioning. Modern research directions have pushed the performance of GNSS localization to new heights by fusing GNSS measurements with other sensory information, mainly measurements from Inertial Measurement Units (IMU). In this paper, we propose a loosely coupled architecture to integrate GNSS and IMU measurements using a Factor Graph Optimization (FGO) framework. Because the FGO method can be computationally challenging and often used as a post-processing method, our focus is on assessing its localization accuracy and service availability while operating in real-time in challenging environments (urban canyons). Experimental results on the UrbanNav-HK-MediumUrban-1 dataset show that the proposed approach achieves real-time operation and increased service availability compared to batch FGO methods. While this improvement comes at the cost of reduced positioning accuracy, the paper provides a detailed analysis of the trade-offs between accuracy, availability, and computational efficiency that characterize real-time FGO-based GNSS/IMU fusion.

Problem

• GNSS/IMU integration
• real-time positioning
• Factor Graph Optimization
• urban canyon
• service availability

Innovation

• Factor Graph Optimization
• Loosely Coupled GNSS/IMU
• Real-time Integration
• Service Availability
• Urban Canyon Navigation