Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910040185634816 |
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| author | Cioaca, Radu-Andrei Rusu, Cristian Irofti, Paul Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin |
| author_facet | Cioaca, Radu-Andrei Rusu, Cristian Irofti, Paul Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03546 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization Cioaca, Radu-Andrei Rusu, Cristian Irofti, Paul Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin Robotics Machine Learning Systems and Control 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. |
| title | Real-time loosely coupled GNSS and IMU integration via Factor Graph Optimization |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2603.03546 |