Advanced Technologies for Indoor Localization and Navigation

Dear Colleagues,

Indoor localization and navigation have become fundamental components of intelligent systems, supporting a wide range of applications such as smart buildings, healthcare, industrial automation, and robotics. With the rapid development of artificial intelligence (AI), wireless sensing, and embedded systems, indoor positioning technologies are evolving from traditional signal-based estimation toward intelligent spatial perception and decision-making.

Despite significant progress having been made, achieving robust and scalable indoor localization still remains challenging. Indoor environments are characterized by complex signal propagation, multipath effects, non-line-of-sight (NLoS) conditions, and dynamic changes, which severely degrade positioning accuracy and reliability. Moreover, many conventional approaches depend on labor-intensive data collection, site surveys, and manual calibration, limiting their scalability and adaptability. The heterogeneity of devices and sensing modalities further complicates model generalization across different environments.

Recent advances in AI have provided new opportunities to address these challenges. Data-driven approaches, including deep learning, self-supervised learning, and generative models, enable more robust feature extraction and representation learning from complex multimodal data. Meanwhile, multi-source sensor fusion techniques—integrating WiFi, Bluetooth, ultra-wideband (UWB), geomagnetic signals, pseudolites, inertial measurement units (IMUs), and vision sensors—are increasingly adopted to enhance localization accuracy and robustness. The emerging research also extends beyond positioning to include indoor scene understanding, spatial modeling, and environment-aware navigation, forming a unified paradigm of spatial intelligence.

Ongoing research is driving indoor localization and navigation systems toward higher accuracy, robustness, scalability, and intelligence, enabling their integration into a wide range of real-world applications.

The topics of interest include, but are not limited to, the following:

• AI-driven indoor localization and navigation techniques.
• WiFi, Bluetooth, UWB, geomagnetic, and pseudolite-based positioning.
• Multi-source sensor fusion (e.g., IMU, wireless signals, vision, LiDAR, magnetic sensing).
• Deep learning, self-supervised learning, sparse learning, and generative models for data-efficient localization.
• SLAM, mapping, and spatial modeling for indoor environments.
• Indoor scene recognition and environment understanding.
• Uncertainty-aware localization and reliability estimation.
• Applications in robotics, smart cities, healthcare, and industrial IoT.

Dr. Meng Sun
Dr. David Plets
Guest Editors

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