# GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals

arXiv: https://arxiv.org/abs/2506.11534 · HTML: https://mlsanity.com/paper/2506.11534/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [A micro Lie theory for state estimation in robotics](https://mlsanity.com/paper/1812.01537/) (2018)
- [On-Manifold Preintegration for Real-Time Visual-Inertial Odometry](https://mlsanity.com/paper/1512.02363/) (2015)
- [PyPose: A Library for Robot Learning with Physics-based Optimization](https://mlsanity.com/paper/2209.15428/) (2022)
- [The invariant extended Kalman filter as a stable observer](https://mlsanity.com/paper/1410.1465/) (2014)
- [Inertial-Only Optimization for Visual-Inertial Initialization](https://mlsanity.com/paper/2003.05766/) (2020)
- [Tightly-coupled Fusion of Global Positional Measurements in Optimization-based Visual-Inertial Odometry](https://mlsanity.com/paper/2003.04159/) (2020)
- [Closed-form Preintegration Methods for Graph-based Visual-Inertial Navigation](https://mlsanity.com/paper/1805.02774/) (2018)
- [Camera Motion Estimation from RGB-D-Inertial Scene Flow](https://mlsanity.com/paper/2404.17251/) (2024)
- [A New Technique for INS/GNSS Attitude and Parameter Estimation Using Online Optimization](https://mlsanity.com/paper/1403.2174/) (2014)

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

- [DefVINS: Visual-Inertial Odometry for Deformable Scenes](https://mlsanity.com/paper/2601.00702/) (2026)

## Abstract (arXiv)

Initializing the state of a sensorized platform can be challenging, as a limited set of measurements often provide low-informative constraints that are in addition highly non-linear. This may lead to poor initial estimates that may converge to local minima during subsequent non-linear optimization. We propose an adaptive GNSS-inertial initialization strategy that delays the incorporation of global GNSS constraints until they become sufficiently informative. In the initial stage, our method leverages inter-epoch baseline vector residuals between consecutive GNSS fixes to mitigate inertial drift. To determine when to activate global constraints, we introduce a general criterion based on the evolution of the Hessian matrix's singular values, effectively quantifying system observability. Experiments on EuRoC, GVINS and MARS-LVIG datasets show that our approach consistently outperforms the naive strategy of fusing all measurements from the outset, yielding more accurate and robust initializations.