Pedestrian Inertial Navigation: An Overview of Model and Data-Driven Approaches

TL;DR

This paper reviews model-based and data-driven inertial navigation methods for indoor pedestrian positioning, highlighting recent approaches and their applications in various fields.

Contribution

It provides a comprehensive overview of inertial pedestrian navigation models and introduces recent data-driven strategies and algorithms.

Findings

Various inertial sensor-based navigation models discussed

Data-driven approaches like activity-assisted and hybrid methods analyzed

The paper highlights recent advancements in pedestrian dead reckoning techniques

Abstract

The task of indoor positioning is fundamental to several applications, including navigation, healthcare, location-based services, and security. An emerging field is inertial navigation for pedestrians, which relies only on inertial sensors for positioning. In this paper, we present inertial pedestrian navigation models and learning approaches. Among these, are methods and algorithms for shoe-mounted inertial sensors and pedestrian dead reckoning (PDR) with unconstrained inertial sensors. We also address three categories of data-driven PDR strategies: activity-assisted, hybrid approaches, and learning-based frameworks.