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Indoor Localization Technologies and Applications
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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: closed (25 March 2026) | Viewed by 11955

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 250 words) can be sent to the Editorial Office for assessment.

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

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Published Papers (6 papers)

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Research

21 pages, 5907 KB  
Article
Indoor Localization Algorithm Based on Information Gain Ratio and Affinity Propagation Clustering
by Rencheng Jin, Di Zhang, Xiao Tian and Jianping Ma
Sensors 2026, 26(2), 664; https://doi.org/10.3390/s26020664 - 19 Jan 2026
Viewed by 554
Abstract
In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the [...] Read more.
In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the improvement in positioning accuracy leveling off as the number of redundant APs increases, while also increasing the computational load of indoor positioning services. To address this problem, the thesis proposes a method for calculating the AP location discrimination capability and combines the location discrimination capability with coverage to eliminate redundant APs. Experiments conducted in real indoor scenarios, as well as on the Crowdsourced dataset and the SODIndoorLoc dataset, validate the results. The results show that the redundant AP removing strategy ensures that the average positioning accuracy fluctuates by no more than 5% compared to the unfiltered case, while significantly reducing the number of APs in the fingerprint database—by 64.43%, 72.78%, and 59.62%, respectively. In the position estimation phase, this paper uses affinity propagation clustering for coarse positioning and combines Bayesian methods for fine positioning. Compared with GMM, K-Means, and the pointwise algorithm, the average positioning error of the proposed method is reduced by 11% to 39%. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
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21 pages, 8490 KB  
Article
BDGS-SLAM: A Probabilistic 3D Gaussian Splatting Framework for Robust SLAM in Dynamic Environments
by Tianyu Yang, Shuangfeng Wei, Jingxuan Nan, Mingyang Li and Mingrui Li
Sensors 2025, 25(21), 6641; https://doi.org/10.3390/s25216641 - 30 Oct 2025
Viewed by 3424
Abstract
Simultaneous Localization and Mapping (SLAM) utilizes sensor data to concurrently construct environmental maps and estimate its own position, finding wide application in scenarios like robotic navigation and augmented reality. SLAM systems based on 3D Gaussian Splatting (3DGS) have garnered significant attention due to [...] Read more.
Simultaneous Localization and Mapping (SLAM) utilizes sensor data to concurrently construct environmental maps and estimate its own position, finding wide application in scenarios like robotic navigation and augmented reality. SLAM systems based on 3D Gaussian Splatting (3DGS) have garnered significant attention due to their real-time, high-fidelity rendering capabilities. However, in real-world environments containing dynamic objects, existing 3DGS-SLAM methods often suffer from mapping errors and tracking drift due to dynamic interference. To address this challenge, this paper proposes BDGS-SLAM—a Bayesian Dynamic Gaussian Splatting SLAM framework specifically designed for dynamic environments. During the tracking phase, the system integrates semantic detection results from YOLOv5 to build a dynamic prior probability model based on Bayesian filtering, enabling accurate identification of dynamic Gaussians. In the mapping phase, a multi-view probabilistic update mechanism is employed, which aggregates historical observation information from co-visible keyframes. By introducing an exponential decay factor to dynamically adjust weights, this mechanism effectively restores static Gaussians that were mistakenly culled. Furthermore, an adaptive dynamic Gaussian optimization strategy is proposed. This strategy applies penalizing constraints to suppress the negative impact of dynamic Gaussians on rendering while avoiding the erroneous removal of static Gaussians and ensuring the integrity of critical scene information. Experimental results demonstrate that, compared to baseline methods, BDGS-SLAM achieves comparable tracking accuracy while generating fewer artifacts in rendered results and realizing higher-fidelity scene reconstruction. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
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28 pages, 3007 KB  
Article
Mobile Robot Localization Based on the PSO Algorithm with Local Minima Avoiding the Fitness Function
by Božidar Bratina, Dušan Fister, Suzana Uran, Izidor Mlakar, Erik Rot Weiss, Kristijan Korez and Riko Šafarič
Sensors 2025, 25(20), 6283; https://doi.org/10.3390/s25206283 - 10 Oct 2025
Cited by 1 | Viewed by 862
Abstract
Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, [...] Read more.
Localization of a semi-humanoid mobile robot Pepper is proposed based on the particle swarm optimization algorithm (PSO) that is robust to the disturbance perturbations of LIDAR-measured distances from the mobile robot to the walls of the robot real laboratory workspace. The novel PSO, with the avoiding local minima algorithm (PSO-ALM), uses a novel fitness function that can prevent the PSO search from trapping into the local minima and thus prevent the mobile robot from misidentifying the actual location. The fitness function penalizes nonsense solutions by introducing continuous integrity checks of solutions between two different consecutive locations. The proposed methodology enables accurate and real-time global localization of a mobile robot, given the underlying a priori map, with a consistent and predictable time complexity. Numerical simulations and real-world laboratory experiments with different a priori map accuracies have been conducted to prove the proper functioning of the method. The results have been compared with the benchmarks, i.e., the plain vanilla PSO and the built-in robot’s odometrical method, a genetic algorithm with included elitism and adaptive mutation rate (GA), the same GA algorithm with the included ALM algorithm (GA-ALM), the state-of-the-art plain vanilla golden eagle optimization (GEO) algorithm, and the same GEO algorithm with the added ALM algorithm (GEO-ALM). The results showed similar performance with the odometrical method right after recalibration and significantly better performance after some traveled distance. The GA and GEO algorithms with or without the ALM extension gave us similar results according to the accuracy of localization. The optimization algorithms’ performance with added ALM algorithms was much better at not getting caught in the local minimum, while the PSO-ALM algorithm gave us the overall best results. Full article
(This article belongs to the Special Issue Indoor Localization Technologies and Applications)
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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
Cited by 1 | Viewed by 2887
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)
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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
Cited by 1 | Viewed by 2012
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)
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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 2 | Viewed by 1374
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)
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