Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911483702542336 |
|---|---|
| author | Cioaca, Radu-Andrei Irofti, Paul Rusu, Cristian Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin |
| author_facet | Cioaca, Radu-Andrei Irofti, Paul Rusu, Cristian Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03556 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization Cioaca, Radu-Andrei Irofti, Paul Rusu, Cristian Caparra, Gianluca Marinache, Andrei-Alexandru Stoican, Florin Robotics Machine Learning Systems and Control 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. |
| title | Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization |
| topic | Robotics Machine Learning Systems and Control |
| url | https://arxiv.org/abs/2603.03556 |