Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization
This work addresses positioning challenges for autonomous vehicles and robotics in urban areas, representing an incremental improvement by enabling real-time operation from an offline baseline.
The paper tackled the problem of reliable positioning in dense urban environments by developing a real-time tightly coupled GNSS-IMU integration method using factor graph optimization, achieving robust state estimation in GNSS-degraded conditions as evaluated on the UrbanNav dataset.
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.