Factor Graph Optimization for GNSS/INS Integration: A Comparison with the Extended Kalman Filter

Wen W., Pfeifer T., Bai X., Hsu L. T.

NAVIGATION: Journal of the Institute of Navigation (2021)

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The EKF estimates only the current epoch; factor graph optimization jointly optimizes a window of past states with re-linearization, giving more accurate and robust GNSS/INS integration.

Key idea. Classical GNSS/INS integration uses an Extended Kalman Filter, which condenses all history into a single current state. This paper recasts the problem as factor graph optimization (FGO) — jointly optimizing a sliding window of states with re-linearization, so the estimator can revisit past epochs and handle outliers far more flexibly.

Impact. It became the reference comparison establishing why FGO outperforms filtering for navigation, and was named the 2024 Most-Cited Paper in NAVIGATION. The result reframed how the field approaches GNSS/INS fusion and underpins much of IPNL’s later integrity and multi-sensor work.