Previous Abstract Return to Session A1 Next Abstract

Session A1: Cutting-Edge Inertial-Based Pedestrian Localization

Constrained Factor Graph Optimization for Robust Networked Pedestrian Inertial Navigation
Yingjie Hu, University of Minnesota, Twin Cities; Wang Hu, University of California, Riverside
Location: Grand Ballroom ABC
Date/Time: Tuesday, Apr. 29, 8:57 a.m.

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 soft-max-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.
Index Terms—Factor Graph Optimization, Inertial Navigation, Constrained Optimization, Kalman Filter, Pedestrian Navigation

Previous Abstract Return to Session A1 Next Abstract