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.
AI and Machine Learning Driven Indoor Localization and Navigation with Mobile Embedded Systems
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abstract
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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GATE: Graph Attention Neural Networks with Real-Time Edge Construction for Robust Indoor Localization using Mobile Embedded Devices
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.