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Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges.

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Summary

This study presents AI-enhanced inertial navigation for resource-constrained Internet-of-Things (IoT) platforms. It introduces lightweight, physics-informed models for accurate localization in GPS-denied environments, overcoming limitations of traditional methods.

Keywords:
BayesianTinyMLdead-reckoninginertialkalman filteringneural architecture searchneural networksneurosymbolicodometryplatform-awaresequence learning

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Area of Science:

  • Robotics and Embedded Systems
  • Artificial Intelligence
  • Sensor Fusion

Background:

  • Inertial navigation offers low-cost localization for Internet-of-Things (IoT) devices in GPS-denied areas.
  • Traditional methods struggle with environmental complexities and drift.
  • AI-based approaches show promise but are resource-intensive and lack physical guarantees.

Purpose of the Study:

  • To explore methods for deploying real-time AI-enhanced inertial navigation on IoT platforms.
  • To develop lightweight, efficient, and accurate AI models for inertial odometry.
  • To address challenges in domain adaptation and data efficiency for AI-driven navigation.

Main Methods:

  • Platform-aware neural architecture search for ultra-lightweight deep neural-inertial odometry models.
  • Neurosymbolic AI techniques to create physics-informed and interpretable navigation models.
  • Efficient data collection and labeling strategies for domain-specific model fine-tuning.

Main Results:

  • Developed neural-inertial odometry models 31-134× smaller than state-of-the-art AI methods, with comparable or superior localization resolution.
  • Generated models applicable to diverse platforms including humans, animals, underwater sensors, aerial vehicles, and robots.
  • Demonstrated effective domain adaptation using minimal labeled data (1 minute).

Conclusions:

  • AI-enhanced inertial navigation can be effectively deployed on resource-constrained IoT platforms.
  • Lightweight, neurosymbolic, and domain-adaptive models offer a viable path for robust localization.
  • Further research is needed to address open challenges in real-world AI-driven navigation systems.