通过约束因子图优化提升行人惯性导航的精度与鲁棒性
Constrained Factor Graph Optimization for Robust Networked Pedestrian Inertial Navigation
- 将人体运动学约束融入非线性优化框架，直接控制漂移
- 引入可微软最大值惩罚项，有效处理肢体传感器间距限制
- 适合高精度行人定位场景，尤其在无外部信号环境下
本文提出一种基于约束因子图优化（Constrained FGO）的新方法，用于网络化行人惯性导航。为有效缓解惯性导航固有的漂移问题，将运动学约束直接嵌入非线性优化框架中。具体包括等式约束（如零速度更新ZUPT）和基于人体解剖学限制的不等式约束（即体装惯性测量单元IMUs间最大允许距离）。等式约束可直接作为误差因子集成，而不等式约束在标准FGO中无法显式表达。为此，本文在成本函数中引入可微软最大值惩罚项，实现不等式约束的平滑且鲁棒的强制执行。所提方法利用多时隙间的时序相关性，获得最优状态轨迹估计，并始终满足约束条件。实验结果表明，该方法优于传统卡尔曼滤波，验证了其在行人导航中的有效性与鲁棒性。
原文摘要 · Abstract (English)
This paper presents a novel constrained Factor Graph Optimization (FGO)-based approach for networked inertial navigation in pedestrian localization. To effectively mitigate the drift inherent in inertial navigation solutions, we incorporate kinematic constraints directly into the nonlinear optimization framework. Specifically, we utilize equality constraints, such as Zero-Velocity Updates (ZUPTs), and inequality constraints representing the maximum allowable distance between body-mounted Inertial Measurement Units (IMUs) based on human anatomical limitations. While equality constraints are straightforwardly integrated as error factors, inequality constraints cannot be explicitly represented in standard FGO formulations. To address this, we introduce a differentiable softmax-based penalty term in the FGO cost function to enforce inequality constraints smoothly and robustly. The proposed constrained FGO approach leverages temporal correlations across multiple epochs, resulting in optimal state trajectory estimates while consistently maintaining constraint satisfaction. Experimental results confirm that our method outperforms conventional Kalman filter approaches, demonstrating its effectiveness and robustness for pedestrian navigation.
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