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

Swapnil Sayan Saha, Yayun Du, Sandeep Singh Sandha, Luis Antonio Garcia, Mohammad Khalid Jawed, Mani Srivastava

Abstract: Abstract—Inertial navigation provides a small footprint, low-power, and low-cost pathway for localization in GPS-denied environments on extremely resource-constrained Internet-of-Things (IoT) platforms. Traditionally, application-specific heuristics and physics-based kinematic models are used to mitigate the curse of drift in inertial odometry. These techniques, albeit lightweight, fail to handle domain shifts and environmental non-linearities. Recently, deep neural-inertial sequence learning has shown superior odometric resolution in capturing non-linear motion dynamics without human knowledge over heuristic-based methods. These AI-based techniques are data-hungry, suffer from excessive resource usage, and cannot guarantee following the underlying system physics. This paper highlights the unique methods, opportunities, and challenges in porting real-time AI-enhanced inertial navigation algorithms onto IoT platforms. First, we discuss how platform-aware neural architecture search coupled with ultra-lightweight model backbones can yield neural-inertial odometry models that are 31-134× smaller yet achieve or exceed the localization resolution of state-of-the-art AI-enhanced techniques. The framework can generate models suitable for locating humans, animals, underwater sensors, aerial vehicles, and precision robots. Next, we showcase how techniques from neurosymbolic AI can yield physics-informed and interpretable neural-inertial navigation models. Afterward, we present opportunities for fine-tuning pre-trained odometry models in a new domain with as little as 1 minute of labeled data, while discussing inexpensive data collection and labeling techniques. Finally, we identify several open research challenges that demand careful consideration moving forward. Index Terms—Bayesian, dead-reckoning, inertial, kalman filtering, neural architecture search, neural networks, neurosymbolic, odometry, platform-aware, sequence learning, TinyML
Published in: 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS)
April 24 - 27, 2023
Hyatt Regency Hotel
Monterey, CA
Pages: 708 - 723
Cite this article: Saha, Swapnil Sayan, Du, Yayun, Sandha, Sandeep Singh, Garcia, Luis Antonio, Jawed, Mohammad Khalid, Srivastava, Mani, "Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges," 2023 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA, April 2023, pp. 708-723.
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