PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms

2026-01-06

arXiv.org

AI Summary

This work addresses the challenge of significant drift in pure inertial navigation under GNSS-denied and vision-deprived environments, where sensor noise severely degrades localization accuracy and existing deep learning approaches often lack physical consistency and interpretability. To overcome these limitations, we propose PiDR, a novel framework that explicitly embeds inertial navigation dynamics into a neural network architecture. By incorporating physics-informed residual constraints during training, PiDR ensures that estimated trajectories adhere to fundamental physical laws. The method achieves transparent, interpretable, and robust dead reckoning with only sparse supervision. Evaluated on real-world datasets from both mobile robots and autonomous underwater vehicles, PiDR improves localization accuracy by over 29% on average, demonstrating strong cross-platform generalization and practical potential for real-time deployment.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Computer Vision: Low Level & Physics-based Vision

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pure inertial navigation. However, the inherent noise and other error terms of the inertial sensors in such real-world scenarios will cause the navigation solution to drift over time. Although conventional deep-learning models have emerged as a possible approach to inertial navigation, they are inherently black-box in nature. Furthermore, they struggle to learn effectively with limited supervised sensor data and often fail to preserve physical principles. To address these limitations, we propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms in situations of pure inertial navigation. PiDR offers transparency by explicitly integrating inertial navigation principles into the network training process through the physics-informed residual component. PiDR plays a crucial role in mitigating abrupt trajectory deviations even under limited or sparse supervision. We evaluated PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle. We obtained more than 29% positioning improvement in both datasets, demonstrating the ability of PiDR to generalize different platforms operating in various environments and dynamics. Thus, PiDR offers a robust, lightweight, yet effective architecture and can be deployed on resource-constrained platforms, enabling real-time pure inertial navigation in adverse scenarios.

Problem

• inertial navigation
• sensor drift
• physics-informed learning
• autonomous platforms
• dead reckoning

Innovation

• Physics-Informed Learning
• Inertial Dead Reckoning
• Autonomous Navigation
• Sensor Fusion
• Deep Learning for Robotics