PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms

TL;DR

PiDR is a physics-informed deep learning framework that enhances inertial navigation accuracy for autonomous platforms operating without external signals, effectively reducing drift and improving position estimates in challenging environments.

Contribution

The paper introduces PiDR, a novel physics-informed deep learning approach that explicitly incorporates inertial navigation principles, improving accuracy and robustness in pure inertial navigation tasks.

Findings

Achieved over 29% position accuracy improvement on real-world datasets.

Demonstrated generalization across different platforms and environments.

Enabled real-time inertial navigation on resource-constrained devices.

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.