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.

翻译：实现完全自主的一个基本要求是在缺乏外部数据（如GNSS信号或视觉信息）的情况下仍能保持精确导航。在这些具有挑战性的环境中，平台必须完全依赖惯性传感器，从而进行纯惯性导航。然而，在此类实际场景中，惯性传感器固有的噪声及其他误差项会导致导航解随时间推移而发生漂移。尽管传统的深度学习模型已成为惯性导航的一种可能途径，但其本质上是黑盒模型。此外，它们在有限的监督传感器数据下难以有效学习，且往往无法保持物理原理。为应对这些局限性，我们提出了PiDR，一种面向自主平台在纯惯性导航情境下的物理信息惯性航位推算框架。PiDR通过物理信息残差组件将惯性导航原理显式地整合到网络训练过程中，从而提供了透明度。即使在有限或稀疏的监督下，PiDR在缓解轨迹突变偏差方面也起着关键作用。我们在由移动机器人和自主水下航行器采集的真实世界数据集上评估了PiDR。在两个数据集中均获得了超过29%的定位性能提升，这证明了PiDR能够泛化到在不同环境与动态条件下运行的各种平台。因此，PiDR提供了一种鲁棒、轻量且高效的架构，可部署在资源受限的平台上，从而在不利场景下实现实时的纯惯性导航。