Loosely Coupled Factor Graph Optimization for Pseudolite-Augmented Navigation
For navigation in GNSS-degraded environments, this work provides an incremental improvement by integrating pseudolites into an optimization-based framework, showing moderate accuracy gains.
This paper presents a loosely coupled factor graph optimization framework that fuses GNSS/pseudolite least-squares solutions with IMU data, achieving a 22.8% to 41.3% reduction in mean 3D error compared to standard least-squares methods in GNSS-degraded environments.
In Global Navigation Satellite System (GNSS)-degraded environments, pseudolites (PLs) provide additional signal sources to enhance positioning performance, but their integration in optimization-based frameworks remains limited. This paper presents a loosely coupled factor graph optimization (FGO) framework that fuses the GNSS/PL least-squares (LS) solutions with inertial measurement unit (IMU) data. The evaluation considers low GNSS visibility scenarios with four high-elevation GNSS satellites and up to two PL transmitters over an 80~s window. FGO achieves a 22.8\% to 41.3\% reduction in mean 3D error compared to standard LS methods. Compared to a GNSS-IMU baseline, incorporating PL transmitters further improves positioning accuracy, with performance depending on geometry.