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

Radu-Andrei Cioaca, Paul Irofti, Cristian Rusu, Gianluca Caparra, Andrei-Alexandru Marinache, Florin Stoican

TL;DR

This work presents a real-time tightly coupled GNSS-IMU FGO method that enables causal state estimation via incremental optimization with fixed-lag marginalization, and evaluates its performance in a highly urbanized GNSS-degraded environment using the UrbanNav dataset.

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.

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

TL;DR

This work presents a real-time tightly coupled GNSS-IMU FGO method that enables causal state estimation via incremental optimization with fixed-lag marginalization, and evaluates its performance in a highly urbanized GNSS-degraded environment using the UrbanNav dataset.

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.
Paper Structure (12 sections, 10 equations, 6 figures, 2 tables)

This paper contains 12 sections, 10 equations, 6 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Factor Graphs
  3. Tightly Coupled GNSS/IMU via FGO
  4. State vector
  5. Factors
  6. Optimization cost
  7. Standard FGO Approach (SFGO)
  8. Proposed Approach: RTFGO Tightly Coupled
  9. Experimental Results
  10. Data Source
  11. Results
  12. Conclusions

Figures (6)

  • Figure 1: Tightly coupled GNSS/IMU integration scheme.
  • Figure 2: Tightly coupled GNSS and IMU factor graph architecture.
  • Figure 3: Number of tracked satellites (along Loops 1 and 2).
  • Figure 4: Comparison of SFGO and RTFGO performance against GNSS-only and ground truth data (Loops 1 and 2)
  • Figure 5: 2D Service Availability
  • ...and 1 more figures