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AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems

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arxiv 2408.04797 v1 pith:PUWJRBQP submitted 2024-08-09 cs.LG cs.AIcs.RO

Sudeep Pasricha This is my paper · ORCID
classification cs.LGcs.AIcs.RO
keywords indoornavigationembeddedlocalizationmobilesystemschallengessignals
verification ladder T0 review T1 audit T2 compute T3 formal
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Indoor navigation is a foundational technology to assist the tracking and localization of humans, autonomous vehicles, drones, and robots in indoor spaces. Due to the lack of penetration of GPS signals in buildings, subterranean locales, and dense urban environments, indoor navigation solutions typically make use of ubiquitous wireless signals (e.g., WiFi) and sensors in mobile embedded systems to perform tracking and localization. This article provides an overview of the many challenges facing state-of-the-art indoor navigation solutions, and then describes how AI algorithms deployed on mobile embedded systems can overcome these challenges.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A dynamic-graph GNN with element-wise attention (GATE) reports sub-2-meter mean Wi-Fi localization error across heterogeneous phones and buildings, outperforming published baselines.

  2. Towards Explainable Indoor Localization: Interpreting Neural Network Learning on Wi-Fi Fingerprints Using Logic Gates

    cs.LG 2025-06 reject novelty 4.0 of 10

    LogNet replaces neural network layers with fixed logic gates on binarized Wi-Fi signals, reporting lower localization error and improved interpretability over time.

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