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Sensor Fusion Applications for Navigation and Indoor Positioning
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Sensor Fusion Applications for Navigation and Indoor Positioning

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

Deadline for manuscript submissions: 15 April 2025 | Viewed by 1711

Special Issue Editor

Dr. Jianguo (Jack) Wang

E-Mail Website
Guest Editor
Transport Research Centre, Faculty of Engineering and Information Technology, University of Technology Sydney (UTS), 81 Broadway, Ultimo, NSW 2007, Australia
Interests: sensor fusion for surveying, navigation and perception; robotics and intelligent systems; environmentally friendly transportation and housing; GNSS, IMU, vision and laser sensors modelling and data fusion
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor positioning has many applications, including in navigation, asset tracking, wayfinding, and location-based advertising. The proliferation of indoor positioning has enabled rapid enhancements in these applications, not only in homes, businesses, and medical services, but also for factory automation. Positioning is the core factor that impacts implementation and performance. There are many characteristics that make indoor positioning different from outdoor positioning. In comparison with outdoor environments, indoor environments are more complex an contain multiple objects (such as pieces of equipment, walls, and people) that block GNSS signals, and lead to multi-path and signal delay problems. There are many sensors that can be used for indoor positioning, but all of them have some limitations. Therefore, sensor fusion is employed to combine measurements from multiple sensors for improved positioning accuracy and reliability.

We encourage authors from academia and industry to submit new research results related to sensor fusion for indoor positioning and navigation. The topics include but are not limited to the following:

  • Multiple sensors:
    • Wi-Fi, Bluetooth, ultra-wideband (UWB), radio frequency identification (RFID), etc.
    • Computer vision, light detection and ranging (Lidar), maps or landmarks, etc.
    • Odometers, inertial measurement units (IMUs), magnetic sensors, etc.
  • Sensor fusion methods:
    • Fusion levels: raw data; detections; tracks, etc.
    • Fusion algorithms: KF, EKF, UKF, CNN, fuzzy logic, etc.
  • Indoor positioning applications: navigation, asset tracking, wayfinding, etc.

Dr. Jianguo Jack 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 positioning
  • sensor fusion
  • fusion levels
  • fusion algorithms
  • positioning sensors
  • indoor positioning systems and applications

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Published Papers (2 papers)

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25 pages, 3314 KiB  
Article
KISS—Keep It Static SLAMMOT—The Cost of Integrating Moving Object Tracking into an EKF-SLAM Algorithm
by Nicolas Mandel, Nils Kompe, Moritz Gerwin and Floris Ernst
Sensors 2024, 24(17), 5764; https://doi.org/10.3390/s24175764 - 4 Sep 2024
Viewed by 456
Abstract
The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have [...] Read more.
The treatment of moving objects in simultaneous localization and mapping (SLAM) is a key challenge in contemporary robotics. In this paper, we propose an extension of the EKF-SLAM algorithm that incorporates moving objects into the estimation process, which we term KISS. We have extended the robotic vision toolbox to analyze the influence of moving objects in simulations. Two linear and one nonlinear motion models are used to represent the moving objects. The observation model remains the same for all objects. The proposed model is evaluated against an implementation of the state-of-the-art formulation for moving object tracking, DATMO. We investigate increasing numbers of static landmarks and dynamic objects to demonstrate the impact on the algorithm and compare it with cases where a moving object is mistakenly integrated as a static landmark (false negative) and a static landmark as a moving object (false positive). In practice, distances to dynamic objects are important, and we propose the safety–distance–error metric to evaluate the difference between the true and estimated distances to a dynamic object. The results show that false positives have a negligible impact on map distortion and ATE with increasing static landmarks, while false negatives significantly distort maps and degrade performance metrics. Explicitly modeling dynamic objects not only performs comparably in terms of map distortion and ATE but also enables more accurate tracking of dynamic objects with a lower safety–distance–error than DATMO. We recommend that researchers model objects with uncertain motion using a simple constant position model, hence we name our contribution Keep it Static SLAMMOT. We hope this work will provide valuable data points and insights for future research into integrating moving objects into SLAM algorithms. Full article
(This article belongs to the Special Issue Sensor Fusion Applications for Navigation and Indoor Positioning)
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21 pages, 3130 KiB  
Article
Large-Scale Indoor Camera Positioning Using Fiducial Markers
by Pablo García-Ruiz, Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, Manuel J. Marín-Jiménez and Rafael Medina-Carnicer
Sensors 2024, 24(13), 4303; https://doi.org/10.3390/s24134303 - 2 Jul 2024
Viewed by 753
Abstract
Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing [...] Read more.
Estimating the pose of a large set of fixed indoor cameras is a requirement for certain applications in augmented reality, autonomous navigation, video surveillance, and logistics. However, accurately mapping the positions of these cameras remains an unsolved problem. While providing partial solutions, existing alternatives are limited by their dependence on distinct environmental features, the requirement for large overlapping camera views, and specific conditions. This paper introduces a novel approach to estimating the pose of a large set of cameras using a small subset of fiducial markers printed on regular pieces of paper. By placing the markers in areas visible to multiple cameras, we can obtain an initial estimation of the pair-wise spatial relationship between them. The markers can be moved throughout the environment to obtain the relationship between all cameras, thus creating a graph connecting all cameras. In the final step, our method performs a full optimization, minimizing the reprojection errors of the observed markers and enforcing physical constraints, such as camera and marker coplanarity and control points. We validated our approach using novel artificial and real datasets with varying levels of complexity. Our experiments demonstrated superior performance over existing state-of-the-art techniques and increased effectiveness in real-world applications. Accompanying this paper, we provide the research community with access to our code, tutorials, and an application framework to support the deployment of our methodology. Full article
(This article belongs to the Special Issue Sensor Fusion Applications for Navigation and Indoor Positioning)
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