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

TL;DR

This paper introduces a real-time, tightly coupled GNSS-IMU fusion method using factor graph optimization, enabling causal state estimation in urban environments with degraded GNSS signals.

Contribution

It presents a novel real-time incremental FGO-based GNSS-IMU fusion approach with fixed-lag marginalization for urban navigation.

Findings

Demonstrates robustness in urban GNSS-degraded environments

Achieves real-time performance with causal state estimation

Evaluated successfully on 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.