Intelligent Perception and Robust Positioning Methods in GNSS-Denied Environments

Dear Colleagues, 

The advancement of spatio-temporal intelligence necessitates the ability to achieve intelligent perception and robust positioning in environments where Global Navigation Satellite System (GNSS) signals are unavailable, such as urban canyons, indoors spaces, and underground areas. In recent years, significant progress has been made in addressing this challenge through the following key developments:

• Enhanced sensor integration: Terminal devices now incorporate higher-performance sensors, such as LiDAR, cameras, millimeter-wave radar, inertial measurement units (IMUs), and ultra-wide band (UWB). Simultaneously, the scene increasingly incorporates diverse signal sources, including 5G, Wi-Fi, Bluetooth, and audio.
• Advances in perception and positioning methods: New methods and theories in perception and positioning are flourishing, especially the application of artificial intelligence and deep learning algorithms in these areas, as well as diverse types of fusion algorithms.
• Hardware and functional integration: Continuous advancements in hardware and system integration have led to the development of solutions like integrated communication and navigation systems, integrated lighting and positioning technologies, and integrated navigation and surveillance systems.

These advancements in hardware, infrastructure, and theories provide significant potential and possibilities to achieve increasingly intelligent perception and robust positioning ability in GNSS-denied environments. Therefore, we warmly invite submissions that discuss innovative solutions and advanced technologies related to environmental perception and understanding, multi-source fusion localization, navigation equipment, and GNSS-augmented positioning that can be applied in the aforementioned scenarios. Notably, high-quality works presented in the ninth international UPINLBS (Ubiquitous positioning, Indoor Navigation and Location-Based Services) conference, which was held in Fremantle, Perth, Australia, on the 22nd–25th October 2024 (http://2404305231.p.make.dcloud.portal1.portal.thefastmake.com/), will be recommended to this Special Issue. The UPINLBS conference concentrates on innovative, state-of-the-art solutions and techniques dealing with ubiquitous positioning, indoor navigation, and location-based technologies, and it is one of the most significant and fast-developing scientific activities on a worldwide scale in the field of location-based services.

We invite the contribution of original research articles, as well as review articles, to this Special Issue. Potential topics include, but are not limited to, the following:

• GNSS-based positioning indoors/outdoors;
• Positioning based on wireless sensor networks and opportunity signals, such Wi-Fi, Bluetooth, UWB, 5G, audio, and Digital Terrestrial Multimedia Broadcast (DTMB);
• Inertial navigation systems;
• Vision-based positioning and perception technologies, such as SLAM, 3D reconstruction, augmented reality, and image matching;
• LiDAR-based positioning and perception technologies, such as point cloud data processing methods, objects automatic extraction and classification, and global fine registration of point cloud;
• Multi-source fusion for resilient ubiquitous positioning, such as GNSS+5G positioning, INS/UWB, INS/GNSS, INS/LiDAR, and GNSS/LiDAR/INS;
• Brain-inspired navigation techniques;
• The uses of artificial intelligence and deep learning in perception and positioning;
• Navigation equipment, such as wearable devices for positioning and health monitoring, smart home products, and customized navigation terminals;
• Positioning based on emerging technologies, such as visible light positioning, pseudo-satellites, and millimeter-wave radar;
• Geospatial data crowdsourcing.

Dr. Yue Yu
Dr. Zhaoliang Liu
Dr. Xiaoxiang Cao
Prof. Dr. Liang Chen
Guest Editors

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• spatiotemporal intelligence
• ubiquitous positioning
• global navigation satellite system
• indoor positioning
• intelligent perception
• multi-source fusion
• deep learning
• location-based services