# Adaptive Factor Graph-Based Tightly Coupled GNSS/IMU Fusion for Robust Positionin

arXiv: https://arxiv.org/abs/2511.23017 · HTML: https://mlsanity.com/paper/2511.23017/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 45 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [A General and Adaptive Robust Loss Function](https://mlsanity.com/paper/1701.03077/) (2017)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [GNSS/Multi-Sensor Fusion Using Continuous-Time Factor Graph Optimization for Robust Localization](https://mlsanity.com/paper/2309.11134/) (2023)
- [Open-Source Factor Graph Optimization Package for GNSS: Examples and Applications](https://mlsanity.com/paper/2502.08158/) (2025)
- [Robust Navigation In GNSS Degraded Environment Using Graph Optimization](https://mlsanity.com/paper/1806.08899/) (2018)
- [Time-correlated Window Carrier-phase Aided GNSS Positioning Using Factor Graph Optimization for Urban Positioning](https://mlsanity.com/paper/2109.00683/) (2021)

## Built on by (most cited first, 1 of 1)

- [Real-time tightly coupled GNSS and IMU integration via Factor Graph Optimization](https://mlsanity.com/paper/2603.03556/) (2026)

## Abstract (arXiv)

Reliable positioning in GNSS-challenged environments remains a critical challenge for navigation systems. Tightly coupled GNSS/IMU fusion improves robustness but remains vulnerable to non-Gaussian noise and outliers. We present a robust and adaptive factor graph-based fusion framework that directly integrates GNSS pseudorange measurements with IMU preintegration factors and incorporates the Barron loss, a general robust loss function that unifies several m-estimators through a single tunable parameter. By adaptively down weighting unreliable GNSS measurements, our approach improves resilience positioning. The method is implemented in an extended GTSAM framework and evaluated on the UrbanNav dataset. The proposed solution reduces positioning errors by up to 41% relative to standard FGO, and achieves even larger improvements over extended Kalman filter (EKF) baselines in urban canyon environments. These results highlight the benefits of Barron loss in enhancing the resilience of GNSS/IMU-based navigation in urban and signal-compromised environments.