A systematic review of AI-enabled indoor localization and navigation is presented, focusing on how these advancements address the limitations of GPS-based systems. A PRISMA-based approach was employed to identify relevant peer-reviewed articles in English, ultimately selecting 65 papers from 54 journals involving 253 authors. Bibliometric techniques and keyword clustering reveal an evolving research landscape centered on digital indoor modeling, machine learning, deep learning, sensor fusion, and robotics-based applications. Citation trends confirm growing interest in AI-driven solutions for signal obstruction, multi-floor settings, and real-time navigation, demonstrating a shift toward integrated sensors and IoT devices for enhanced positioning accuracy. Emergent thematic clusters highlight human–machine interaction, including Augmented Reality, emergency management, and healthcare assistance, indicating diverse contexts in which AI-based indoor localization can be adopted. The findings underscore how algorithms such as fingerprinting, neural networks, and Bayesian methods refine performance and reliability. Despite considerable progress, the literature indicates a lack of standardized methodologies and infrastructures, underscoring the continued need for adaptable, cost-effective solutions. Overall, the potential of AI to elevate indoor navigation services through greater precision, robustness, and situational awareness is emphasized, while future work should focus on interdisciplinary collaboration, unified frameworks, and the ethical implications of data-driven systems.
Artificial Intelligence Enabled Indoor Localization and Navigation: A Review
Catalano, Christian;
2025-01-01
Abstract
A systematic review of AI-enabled indoor localization and navigation is presented, focusing on how these advancements address the limitations of GPS-based systems. A PRISMA-based approach was employed to identify relevant peer-reviewed articles in English, ultimately selecting 65 papers from 54 journals involving 253 authors. Bibliometric techniques and keyword clustering reveal an evolving research landscape centered on digital indoor modeling, machine learning, deep learning, sensor fusion, and robotics-based applications. Citation trends confirm growing interest in AI-driven solutions for signal obstruction, multi-floor settings, and real-time navigation, demonstrating a shift toward integrated sensors and IoT devices for enhanced positioning accuracy. Emergent thematic clusters highlight human–machine interaction, including Augmented Reality, emergency management, and healthcare assistance, indicating diverse contexts in which AI-based indoor localization can be adopted. The findings underscore how algorithms such as fingerprinting, neural networks, and Bayesian methods refine performance and reliability. Despite considerable progress, the literature indicates a lack of standardized methodologies and infrastructures, underscoring the continued need for adaptable, cost-effective solutions. Overall, the potential of AI to elevate indoor navigation services through greater precision, robustness, and situational awareness is emphasized, while future work should focus on interdisciplinary collaboration, unified frameworks, and the ethical implications of data-driven systems.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
