GNSS-inertial state initialization by distance residuals
This addresses initialization challenges for sensorized platforms in robotics and navigation, though it appears incremental as it builds on existing GNSS-inertial frameworks.
The paper tackles the problem of poor initial state estimation in GNSS-inertial systems by delaying global GNSS measurements until sufficient information is available, using relative distance residuals initially and a Hessian-based switching criterion. Experiments on EuRoC and GVINS datasets show it consistently outperforms naive early global measurement strategies.
Initializing the state of a sensorized platform can be challenging, as a limited set of initial measurements often carry limited information, leading to poor initial estimates that may converge to local minima during non-linear optimization. This paper proposes a novel GNSS-inertial initialization strategy that delays the use of global GNSS measurements until sufficient information is available to accurately estimate the transformation between the GNSS and inertial frames. Instead, the method initially relies on GNSS relative distance residuals. To determine the optimal moment for switching to global measurements, we introduce a criterion based on the evolution of the Hessian matrix singular values. Experiments on the EuRoC and GVINS datasets show that our approach consistently outperforms the naive strategy of using global GNSS data from the start, yielding more accurate and robust initializations.