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

arXiv: https://arxiv.org/abs/2407.21676 · HTML: https://mlsanity.com/paper/2407.21676/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2024.

## Built on by (most cited first, 4 of 4)

- [MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation](https://mlsanity.com/paper/2507.18206/) (2025)
- [On Neural Inertial Classification Networks for Pedestrian Activity Recognition](https://mlsanity.com/paper/2502.17520/) (2025)
- [Diffusion-Driven Inertial Generated Data for Smartphone Location Classification](https://mlsanity.com/paper/2504.15315/) (2025)
- [ICD-Net: Inertial Covariance Displacement Network for Drone Visual-Inertial SLAM](https://mlsanity.com/paper/2512.00037/) (2025)

## Abstract (arXiv)

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.