Optimization-Based VINS: Consistency, Marginalization, and FEJ
Chuchu Chen, Patrick Geneva, Yuxiang Peng, Woosik Lee, Guoquan Huang
Abstract
In this work, we present a comprehensive analysis of the application of the First-estimates Jacobian (FEJ) design methodology in nonlinear optimization-based Visual-Inertial Navigation Systems (VINS). The FEJ approach fixes system linearization points to preserve proper observability properties of VINS and has been shown to significantly improve the estimation performance of state-of-the-art filtering-based methods. However, its direct application to optimization-based estimators holds challenges and pitfalls, which we addressed in this paper. Specifically, we carefully examine the observability and its relation to inconsistency and FEJ, based on this, we explain how to properly apply and implement FEJ within four marginalization archetypes commonly used in non-linear optimizationbased frameworks. FEJ's effectiveness and applications to VINS are investigated and demonstrate significant performance improvements. Additionally, we offer a detailed discussion of results and guidelines on how to properly implement FEJ in optimization-based estimators.
BibTeX
@inproceedings{iros2023_optimizationbase,
title = {Optimization-Based VINS: Consistency, Marginalization, and FEJ},
author = {Chuchu Chen and Patrick Geneva and Yuxiang Peng and Woosik Lee and Guoquan Huang},
booktitle = {IROS 2023},
year = {2023}
}