# MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation

arXiv: https://arxiv.org/abs/2507.18206 · HTML: https://mlsanity.com/paper/2507.18206/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023)
- [Inertial Navigation Meets Deep Learning: A Survey of Current Trends and Future Directions](https://mlsanity.com/paper/2307.00014/) (2023)
- [Data-Driven Denoising of Stationary Accelerometer Signals](https://mlsanity.com/paper/2206.05937/) (2022)
- [Pedestrian Inertial Navigation: An Overview of Model and Data-Driven Approaches](https://mlsanity.com/paper/2407.21676/) (2024)
- [MoRPI: Mobile Robot Pure Inertial Navigation](https://mlsanity.com/paper/2207.02982/) (2022)
- [Deep Learning Assisted Inertial Dead Reckoning and Fusion](https://mlsanity.com/paper/2407.16387/) (2024)
- [Snake-Inspired Mobile Robot Positioning with Hybrid Learning](https://mlsanity.com/paper/2411.17430/) (2024)

## Built on by (most cited first, 3 of 3)

- [A-PINN: Auxiliary Physics-informed Neural Networks for Structural Vibration Analysis in Continuous Euler-Bernoulli Beam](https://mlsanity.com/paper/2601.00866/) (2025)
- [PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms](https://mlsanity.com/paper/2601.03040/) (2026)
- [DVL-DeepONet: A Physics-Guided Operator Learning for Resilient Underwater Navigation](https://mlsanity.com/paper/2606.23502/) (2026)

## Abstract (arXiv)

A fundamental requirement for full autonomy in mobile robots is accurate navigation even in situations where satellite navigation or cameras are unavailable. In such practical situations, relying only on inertial sensors will result in navigation solution drift due to the sensors' inherent noise and error terms. One of the emerging solutions to mitigate drift is to maneuver the robot in a snake-like slithering motion to increase the inertial signal-to-noise ratio, allowing the regression of the mobile robot position. In this work, we propose MoRPI-PINN as a physics-informed neural network framework for accurate inertial-based mobile robot navigation. By embedding physical laws and constraints into the training process, MoRPI-PINN is capable of providing an accurate and robust navigation solution. Using real-world experiments, we show accuracy improvements of over 85% compared to other approaches. MoRPI-PINN is a lightweight approach that can be implemented even on edge devices and used in any typical mobile robot application.