Optimization-Based VINS: Consistency, Marginalization, and FEJ
Optimization-Based VINS: Consistency, Marginalization, and FEJ
复制标题
DOI：
10.1109/iros55552.2023.10341637
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Chuchu Chen;Patrick Geneva;Yuxiang Peng;W. Lee;Guoquan Huang
中科院分区：
文献类型：
--
作者：
Chuchu Chen;Patrick Geneva;Yuxiang Peng;W. Lee;Guoquan Huang
本文对第一估计雅可比(FEJ)设计方法在基于非线性优化的视觉惯性导航系统(VINS)中的应用进行了全面的分析。FEJ方法固定了系统线性化的点，以保持VINS的适当可观测性，并已被证明显著改善了基于最新滤波的方法的估计性能。然而，它直接应用于基于最优化的估计器存在挑战和陷阱，我们在本文中解决了这一问题。具体地说，我们仔细研究了可观测性及其与不一致性和FEJ的关系，在此基础上，我们解释了如何在基于非线性优化的框架中常用的四个边际化原型中正确地应用和实现FEJ。对FEJ的有效性和在VINS中的应用进行了研究，并显示出显著的性能改进。此外，我们还提供了关于如何在基于优化的估计器中正确实现FEJ的结果和指导方针的详细讨论。
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.