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Sensors and Techniques for Indoor Positioning and Localization: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 407

Special Issue Editor


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Guest Editor
Department of Engineering, University of Perugia, 06125 Perugia, Italy
Interests: indoor and short-range positioning; statistical signal processing; battery characterization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Accurate indoor positioning is an interesting topic whose applications have impacts on various fields, which, depending on the targeted accuracy, can include line traceability, telemanipulation, telemedicine, and drone control. By overcoming the limitations of outdoor GNSS, indoor positioning techniques permit seamless indoor/outdoor positioning and can be a strong enabler for IoT and Industry 4.0 applications. As such, various approaches and methods have been proposed in the literature, with no definite solution being competitive for most conceivable scenarios. At the measurement level, various quantities can be measured, including inertial readings, ultrasound waves, static or AC magnetic fields, radiofrequency EM waves, and image or video recordings. These measurements can be combined using various approaches, such as fingerprinting, sensor fusion between multiple measurements, and, more recently, artificial intelligence. Additional degrees of freedom can be exploited at the system design level, where specific infrastructure can be deployed, based either on specific proprietary technology or on off-the-shelf devices, such as ultrawide-band (UWB) transceivers. Depending on the targeted accuracy and budget, additional tradeoffs can be realized by using pre-existing RF infrastructures, such as WiFi or Bluetooth networks. To this end, new opportunities are offered by modern consumer devices such as tablets and smartphones, equipped with embedded video cameras, multiple sensors, wireless networking capabilities, and powerful processors. The use of such standardized technologies provides a well-established infrastructure for the implementation of distributed systems, at the price of introducing potential cybersecurity vulnerabilities. Hence, this Special Issue welcomes innovative contributions, advancing the state of the art in indoor or short-range positioning with respect to sensors, measurements, estimation techniques, system architectures, devices, security, and applications.

Prof. Dr. Antonio Moschitta
Guest Editor

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Keywords

  • indoor positioning
  • tracking
  • sensors
  • smart sensors
  • algorithms
  • measurement
  • accuracy
  • estimation
  • artificial intelligence
  • Internet of Things

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Published Papers (1 paper)

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Research

16 pages, 3685 KiB  
Article
Enhanced Simultaneous Localization and Mapping Algorithm Based on Deep Learning for Highly Dynamic Environment
by Yin Lu, Haibo Wang, Jin Sun and J. Andrew Zhang
Sensors 2025, 25(8), 2539; https://doi.org/10.3390/s25082539 - 17 Apr 2025
Viewed by 173
Abstract
Visual simultaneous localization and mapping (SLAM) is a critical technology for autonomous navigation in dynamic environments. However, traditional SLAM algorithms often struggle to maintain accuracy in highly dynamic environments, where elements undergo significant, rapid, and unpredictable changes, leading to asymmetric information acquisition. Aiming [...] Read more.
Visual simultaneous localization and mapping (SLAM) is a critical technology for autonomous navigation in dynamic environments. However, traditional SLAM algorithms often struggle to maintain accuracy in highly dynamic environments, where elements undergo significant, rapid, and unpredictable changes, leading to asymmetric information acquisition. Aiming to improve the accuracy of the SLAM algorithm in a dynamic environment, a dynamic SLAM algorithm based on deep learning is proposed. Firstly, YOLOv10n is used to improve the front end of the system, and semantic information is added to each frame of the image. Then, ORB-SLAM2 is used to extract feature points in each region of each frame and retrieve semantic information from YOLOv10n. Finally, through the map construction thread, the dynamic object feature points extracted by YOLOv10n are eliminated, and the construction of a static map is realized. The experimental results show that the accuracy of the proposed algorithm is improved by more than 96% compared with the state-of-the-art ORB-SLAM2 in a highly dynamic environment. Compared with other dynamic SLAM algorithms, the proposed algorithm has improved both accuracy and runtime. Full article
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