Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Indoor Localization Technologies and Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Indoor Localization Technologies and Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Navigation and Positioning".

Deadline for manuscript submissions: 25 March 2026 | Viewed by 2118

Special Issue Editor

Dr. Zhiyong Wang

E-Mail Website
Guest Editor
School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Interests: GIS; spatial modelling; routing problem

Special Issue Information

Dear Colleagues,

This Special Issue focuses on the latest advancements in indoor localization technologies and their applications within indoor environments. As the demand for precise and efficient indoor location-based services grows, this collection highlights research related to positioning systems, algorithms, sensors, and technologies that provide reliable and accurate localization. It also covers the key challenges faced in indoor localization, such as signal interference, scalability, and system integration. In addition, this Special Issue promotes recent research using novel indoor localization techniques for various areas of application, including indoor modeling, indoor navigation, disaster response, robotics, etc.

Keywords:

  • Indoor Localization;
  • Positioning Systems;
  • Indoor Navigation;
  • Wireless Localization;
  • Location-based Services;
  • Indoor GPS Alternatives;
  • Real-time Positioning Systems;
  • Signal Interference;
  • Data Fusion Techniques;
  • Internet of Things (IoT);
  • Indoor Modeling;
  • Disaster Management;
  • Indoor Mobile Robot Navigation.

Dr. Zhiyong Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • indoor localization
  • indoor navigation
  • indoor GPS alternatives
  • indoor mobile robot navigation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

21 pages, 2516 KB  
Article
Risk-Aware Reinforcement Learning with Dynamic Safety Filter for Collision Risk Mitigation in Mobile Robot Navigation
by Bingbing Guo, Guina Wang, Yiyang Chen, Yue Gao and Qian Xie
Sensors 2025, 25(17), 5488; https://doi.org/10.3390/s25175488 - 3 Sep 2025
Viewed by 547
Abstract
Mobile robots face collision risk avoidance challenges in dynamic environments, necessitating that we address the safety and adaptability shortcomings of traditional navigation methods. Traditional methods rely on predefined rules, making it difficult to achieve flexible, safe, and real-time obstacle avoidance in complex, dynamic [...] Read more.
Mobile robots face collision risk avoidance challenges in dynamic environments, necessitating that we address the safety and adaptability shortcomings of traditional navigation methods. Traditional methods rely on predefined rules, making it difficult to achieve flexible, safe, and real-time obstacle avoidance in complex, dynamic environments. To address this issue, a risk-aware, dynamic, adaptive regulation barrier policy optimization (RADAR-BPO) method is proposed, combining proximal policy optimization (PPO) with the control barrier function (CBF). RADAR-BPO generates exploratory actions using PPO and constructs a real-time safety filter using the CBF. This method uses quadratic programming to minimize risky actions, thereby ensuring safe obstacle avoidance while maintaining navigation efficiency. Testing of three phased learning environments in the ROS Gazebo simulation environment demonstrated that the proposed method achieves an obstacle avoidance success rate of nearly 90% in complex, dynamic, multi-obstacle environments and improves the overall mission success rate, validating its robustness and effectiveness in complex dynamic scenarios. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
►▼ Show Figures

Figure 1

23 pages, 4627 KB  
Article
Dynamic SLAM Dense Point Cloud Map by Fusion of Semantic Information and Bayesian Moving Probability
by Qing An, Shao Li, Yanglu Wan, Wei Xuan, Chao Chen, Bufan Zhao and Xijiang Chen
Sensors 2025, 25(17), 5304; https://doi.org/10.3390/s25175304 - 26 Aug 2025
Viewed by 586
Abstract
Most existing Simultaneous Localization and Mapping (SLAM) systems rely on the assumption of static environments to achieve reliable and efficient mapping. However, such methods often suffer from degraded localization accuracy and mapping consistency in dynamic settings, as they lack explicit mechanisms to distinguish [...] Read more.
Most existing Simultaneous Localization and Mapping (SLAM) systems rely on the assumption of static environments to achieve reliable and efficient mapping. However, such methods often suffer from degraded localization accuracy and mapping consistency in dynamic settings, as they lack explicit mechanisms to distinguish between static and dynamic elements. To overcome this limitation, we present BMP-SLAM, a vision-based SLAM approach that integrates semantic segmentation and Bayesian motion estimation to robustly handle dynamic indoor scenes. To enable real-time dynamic object detection, we integrate YOLOv5, a semantic segmentation network that identifies and localizes dynamic regions within the environment, into a dedicated dynamic target detection thread. Simultaneously, the data association Bayesian mobile probability proposed in this paper effectively eliminates dynamic feature points and successfully reduces the impact of dynamic targets in the environment on the SLAM system. To enhance complex indoor robotic navigation, the proposed system integrates semantic keyframe information with dynamic object detection outputs to reconstruct high-fidelity 3D point cloud maps of indoor environments. The evaluation conducted on the TUM RGB-D dataset indicates that the performance of BMP-SLAM is superior to that of ORB-SLAM3, with the trajectory tracking accuracy improved by 96.35%. Comparative evaluations demonstrate that the proposed system achieves superior performance in dynamic environments, exhibiting both lower trajectory drift and enhanced positioning precision relative to state-of-the-art dynamic SLAM methods. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
►▼ Show Figures

Figure 1

26 pages, 5598 KB  
Article
DeepLabV3+-Based Semantic Annotation Refinement for SLAM in Indoor Environments
by Shuangfeng Wei, Hongrui Tang, Changchang Liu, Tong Yang, Xiaohang Zhou, Sisi Zlatanova, Junlin Fan, Liping Tu and Yaqin Mao
Sensors 2025, 25(11), 3344; https://doi.org/10.3390/s25113344 - 26 May 2025
Cited by 1 | Viewed by 550
Abstract
Visual SLAM systems frequently encounter challenges in accurately reconstructing three-dimensional scenes from monocular imagery in semantically deficient environments, which significantly compromises robotic operational efficiency. While conventional manual annotation approaches can provide supplemental semantic information, they are inherently inefficient, procedurally complex, and labor-intensive. This [...] Read more.
Visual SLAM systems frequently encounter challenges in accurately reconstructing three-dimensional scenes from monocular imagery in semantically deficient environments, which significantly compromises robotic operational efficiency. While conventional manual annotation approaches can provide supplemental semantic information, they are inherently inefficient, procedurally complex, and labor-intensive. This paper presents an optimized DeepLabV3+-based framework for visual SLAM that integrates image semantic segmentation with automated point cloud semantic annotation. The proposed method utilizes MobileNetV3 as the backbone network for DeepLabV3+ to maintain segmentation accuracy while reducing computational demands. In this paper, we introduce a parameter-adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm incorporating K-nearest neighbors and accelerated by KD-tree structures, effectively addressing the limitations of manual parameter tuning and erroneous annotations in conventional methods. Furthermore, a novel point cloud processing strategy featuring dynamic radius thresholding is developed to enhance annotation completeness and boundary precision. Experimental results demonstrate that our approach achieves significant improvements in annotation efficiency while preserving high accuracy, thereby providing reliable technical support for enhanced environmental understanding and navigation capabilities in indoor robotic applications. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop