Constrained Factor Graph Optimization for Robust Networked Pedestrian Inertial Navigation

TL;DR

This paper introduces a constrained factor graph optimization method for pedestrian inertial navigation that incorporates kinematic constraints, improving accuracy and robustness over traditional Kalman filter approaches.

Contribution

It develops a novel constrained FGO framework with softmax-based penalties for inequality constraints, enhancing pedestrian navigation accuracy.

Findings

Outperforms Kalman filter-based methods in experiments.

Effectively enforces kinematic constraints in navigation.

Provides robust and accurate state estimates across multiple epochs.

Abstract

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.