Enhancing Indoor LBS with Emerging Sensor Technologies
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".
Deadline for manuscript submissions: 1 July 2024 | Viewed by 1086
Special Issue Editors
Interests: GeoAI; VGI; geoparsing; indoor mapping and localization
Special Issues, Collections and Topics in MDPI journals
Interests: positioning and navigation; activity recognition
Special Issues, Collections and Topics in MDPI journals
Interests: mobile computing; indoor localization; indoor tracking; Internet of Things (IoT); wireless sensor network and robotics
Special Issue Information
Dear Colleagues,
The indoor Location-Based Services (LBS) sector is on a rapid ascent. This Special Issue zeroes in on pivotal sensor-driven technologies—LiDAR, computer vision, IMUs, and wireless techniques like VLC, Bluetooth, and Wi-Fi—which are reshaping indoor mapping and positioning with remarkable accuracy and flexibility. Crucial for applications that range from the precision required in autonomous deliveries to the critical responsiveness needed in emergency services, these sensor technologies are redefining the capabilities of indoor LBS.
By collating cutting-edge research, this issue aspires to bridge the gap between technological potential and practical implementation. It invites contributions that navigate the complexities of indoor environments, illustrating how the confluence of innovative sensors and intelligent data interpretation can redefine indoor LBS. The overarching vision is to foster a dialogue on converting the technical prowess of sensor technology into effective, real-world indoor LBS solutions.
Topics of interest include, but are not limited to:
- High-Precision Indoor Positioning Systems;
- Neural Inertial Localization Techniques;
- Sensor Fusion for Robust Indoor Navigation;
- Energy-Efficient Sensor Networks for Indoor Localization;
- Advanced Sensor Technologies for Indoor Mapping;
- 3D Indoor Environment Reconstruction;
- Simultaneous Localization and Mapping (SLAM);
- Crowdsourced Data for Indoor Mapping;
- Intelligent Floor Plan Analysis and Parsing;
- Innovative Indoor Data Structures and Modeling;
- BIM Integration with Indoor Point Cloud Data;
- Semantic Labeling and Interpretation of Indoor Spaces;
- Machine Learning for Indoor Environmental Understanding;
- Augmented Reality for Interactive Indoor Mapping;
- Neuromorphic Positioning and Navigation based on Neuromorphic Sensors;
- Brain-inspired SLAM based on Neuromorphic Computing.
Dr. Xuke Hu
Prof. Dr. Fuqiang Gu
Dr. Yan Li
Dr. Fangwen Yu
Guest Editors
Manuscript Submission Information
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Keywords
- indoor LBS
- positioning
- mapping
- reconstruction
- navigation
- LiDAR
- computer vision
