GNSS-Inertial State Initialization Using Inter-Epoch Baseline Residuals

TL;DR

This paper presents an adaptive GNSS-inertial initialization method that delays global constraints until sufficient information is available, improving accuracy and robustness in sensor state estimation.

Contribution

The proposed approach introduces a criterion based on Hessian singular values to determine when to incorporate GNSS constraints, enhancing initialization performance.

Findings

Outperforms naive measurement fusion strategies

Provides more accurate initial state estimates

Demonstrates robustness across multiple datasets

Abstract

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.