arXiv:2601.03040v2 Announce Type: replace-cross 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 inertia
The increasing demand for autonomous platforms in GPS-denied or visually challenging environments necessitates more robust and accurate navigation solutions, pushing research in physics-informed AI for inertial dead reckoning.
Achieving reliable autonomous operation in hostile or infrastructure-poor environments is critical for defense, logistics, and exploration, directly impacting national security and economic efficiency.
This development proposes a method to significantly enhance the accuracy and robustness of inertial navigation systems, potentially reducing reliance on external signals and improving autonomy in challenging conditions.
- · Autonomous vehicle manufacturers
- · Defense contractors
- · Logistics companies
- · AI/ML research institutions
Improved reliability and operational range for autonomous platforms operating in GPS-denied environments.
Accelerated development and deployment of autonomous systems in sectors like military, mining, and subsea exploration.
Enhanced resilience of critical infrastructure and supply chains against disruption of traditional navigation systems.
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Read at arXiv cs.LG