Loosely Coupled Factor Graph Optimization for Pseudolite-Augmented Navigation

2026-05-24

AI Summary

This work addresses the significant degradation in positioning accuracy under GNSS-challenged environments by proposing a loosely coupled factor graph optimization framework. For the first time, it integrates pseudolite-augmented GNSS least-squares position estimates with IMU preintegration measurements, achieving a favorable balance between computational efficiency and robustness. The approach operates solely on position-level outputs without requiring high-rate raw GNSS observations, thereby enhancing navigation performance in low-visibility conditions. Experimental results demonstrate that the proposed method reduces 3D average positioning error by 22.8%–41.3% compared to standard least-squares GNSS solutions. Furthermore, the incorporation of pseudolites yields a marked improvement in accuracy over conventional loosely coupled GNSS-IMU integration schemes.

Abstract

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.

Problem

• pseudolite
• GNSS-degraded environments
• positioning accuracy
• loosely coupled integration
• factor graph optimization

Innovation

• Factor Graph Optimization
• Pseudolite
• Loosely Coupled Integration
• GNSS-Degraded Navigation
• IMU Fusion