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Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach
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Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach

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

Deadline for manuscript submissions: closed (30 April 2023) | Viewed by 11260

Special Issue Editors

Prof. Dr. Laura Giarre

E-Mail Website
Guest Editor
Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Via P. Vivarelli, 10, 41125 Modena, Italy
Interests: networked system (wifi networks; sensor networks; and inventory networks); smart grids; identification and learning; assistive technology; robotics
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Federica Pascucci

E-Mail Website
Guest Editor
Dipartimento di Ingegneri, Università di Roma Tre, Via Vito Volterra, 79, 00146 Roma, Italy
Interests: mobile robot perception; data fusion and networked robot
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor localization is very important in many fields, such as localization of tourists, assistive technology, industry monitoring, robotics, autonomous driving, e.g., navigation and planning, marketing and recommendations, and also in risk operations such as firefighter interventions. All these fields have a very high economical, technological, and societal impact.

Indoor positioning is a hot topic these days, and there is a growing need for it in public buildings such as airports, hospitals, universities, or shopping malls. Indoor positioning systems should be accurate, easily available for users, and with a low installation and maintenance cost, which makes development challenging.

Indoor and outdoor localization and navigation has been addressed in literature and applications through different approaches in terms of sensors and methodologies. It is, however, extremely important to address the problem and find solutions that are not only based on a single point of view, such as computer vison, AI, communication technologies, visible light communications, dead reckoning, and so on. Existing systems are tailored to a given environment and usually rely on a single technology.

The main aim of the present Special Issue is to invite researchers to present multidisciplinary solutions that take advantage of connecting and exploiting various methodologies and sensors, such as cameras, depth cameras, UWB anchors, RFIDs, the IoT, radars, lidars, smartphones, ultrasonic, beacons, and so on.

This Special Issue of Sensors aims to collect both reviews and original research papers on advancing technology for localization and navigation. Potential topics include but are not limited to the following:

  • AI
  • Computer vision
  • Robotics
  • Machine Learning
  • Dead-Reckoning
  • IOT
  • UWB
  • Sensor Fusion
  • WIFI
  • Autonomous Driving
  • Smart mobility
  • EKF
  • UKF

Prof. Dr. Laura Giarre
Prof. Dr. Federica Pascucci
Guest Editors

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

  • localization
  • navigation
  • sensors
  • probabilistic approach
  • computer vision
  • machine learning
  • perception
  • AI

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Published Papers (4 papers)

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Research

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11 pages, 2431 KiB  
Communication
Sound Event Localization and Detection Using Imbalanced Real and Synthetic Data via Multi-Generator
by Yeongseo Shin and Chanjun Chun
Sensors 2023, 23(7), 3398; https://doi.org/10.3390/s23073398 - 23 Mar 2023
Cited by 1 | Viewed by 1602
Abstract
This study proposes a sound event localization and detection (SELD) method using imbalanced real and synthetic data via a multi-generator. The proposed method is based on a residual convolutional neural network (RCNN) and a transformer encoder for real spatial sound scenes. SELD aims [...] Read more.
This study proposes a sound event localization and detection (SELD) method using imbalanced real and synthetic data via a multi-generator. The proposed method is based on a residual convolutional neural network (RCNN) and a transformer encoder for real spatial sound scenes. SELD aims to classify the sound event, detect the onset and offset of the classified event, and estimate the direction of the sound event. In Detection and Classification of Acoustic Scenes and Events (DCASE) 2022 Task 3, SELD is performed with a few real spatial sound scene data and a relatively large number of synthetic data. When a model is trained using imbalanced data, it can proceed by focusing only on a larger number of data. Thus, a multi-generator that samples real and synthetic data at a specific rate in one batch is proposed to prevent this problem. We applied the data augmentation technique SpecAugment and used time-frequency masking to the dataset. Furthermore, we propose a neural network architecture to apply the RCNN and transformer encoder. Several models were trained with various structures and hyperparameters, and several ensemble models were obtained by “cherry-picking” specific models. Based on the experiment, the single model of the proposed method and the model applied with the ensemble exhibited improved performance compared with the baseline model. Full article
(This article belongs to the Special Issue Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach)
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12 pages, 2926 KiB  
Article
Victim Detection and Localization in Emergencies
by Carlos S. Álvarez-Merino, Emil J. Khatib, Hao Qiang Luo-Chen and Raquel Barco
Sensors 2022, 22(21), 8433; https://doi.org/10.3390/s22218433 - 2 Nov 2022
Cited by 4 | Viewed by 2497
Abstract
Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow [...] Read more.
Detecting and locating victims in emergency scenarios comprise one of the most powerful tools to save lives. Fast actions are crucial for victims because time is running against them. Radio devices are currently omnipresent within the physical proximity of most people and allow locating buried victims in catastrophic scenarios. In this work, we present the benefits of using WiFi Fine Time Measurement (FTM), Ultra-Wide Band (UWB), and fusion technologies to locate victims under rubble. Integrating WiFi FTM and UWB in a drone may cover vast areas in a short time. Moreover, the detection capacity of WiFi and UWB for finding individuals is also compared. These findings are then used to propose a method for detecting and locating victims in disaster scenarios. Full article
(This article belongs to the Special Issue Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach)
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16 pages, 5054 KiB  
Article
Localization in Structured Environments with UWB Devices without Acceleration Measurements, and Velocity Estimation Using a Kalman–Bucy Filter
by Francesco Alonge, Pasquale Cusumano, Filippo D’Ippolito, Giovanni Garraffa, Patrizia Livreri and Antonino Sferlazza
Sensors 2022, 22(16), 6308; https://doi.org/10.3390/s22166308 - 22 Aug 2022
Cited by 11 | Viewed by 2234
Abstract
In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be [...] Read more.
In this work, a novel scheme for velocity and position estimation in a UWB range-based localization system is proposed. The suggested estimation strategy allows to overcome two main problems typically encountered in the localization systems. The first one is that it can be suitable for use in environments where the GPS signal is not present or where it might fail. The second one is that no accelerometer measurements are needed for the localization task. Moreover, to deal with the velocity estimation problem, a suitable Kalman–Bucy filter is designed and it is compared, experimentally, with a particle filter by showing the features of the two algorithms in order to be used in a localization context. Additionally, further experimental tests are carried out on a suitable developed test setup in order to confirm the goodness of the proposed approach. Full article
(This article belongs to the Special Issue Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach)
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Other

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25 pages, 38551 KiB  
Tutorial
Tutorial on High-Definition Map Generation for Automated Driving in Urban Environments
by Jinseop Jeong, Jun Yong Yoon, Hwanhong Lee, Hatem Darweesh and Woosuk Sung
Sensors 2022, 22(18), 7056; https://doi.org/10.3390/s22187056 - 18 Sep 2022
Cited by 6 | Viewed by 3852
Abstract
High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond [...] Read more.
High-definition (HD) mapping is a promising approach to realize highly automated driving (AD). Although HD maps can be applied to all levels of autonomy, their use is particularly beneficial for autonomy levels 4 or higher. HD maps enable AD systems to see beyond the field of view of conventional sensors, thereby providing accurate and detailed information regarding a driving environment. An HD map is typically separated into a pointcloud map for localization and a vector map for path planning. In this paper, we introduce two separate but successive HD map generation workflows. Of the several stages involved, the registration and mapping processes are essential for creating the pointcloud and vector maps, respectively. To facilitate the readers’ understanding, the processes of these two stages have been recorded and uploaded online. HD maps are typically generated using open-source software (OSS) tools. CloudCompare and ASSURE, as representative tools, are used in this study. The generated HD maps are validated with localization and path-planning modules in Autoware, which is also an OSS stack for AD systems. The generated HD maps enable environmental-monitoring vehicles to successfully operate at level 4 autonomy. Full article
(This article belongs to the Special Issue Indoor and Outdoor Localization and Navigation—a Multidisciplinary Approach)
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