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Special Issue "Advances in Indoor Positioning and Indoor Navigation"

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 11871

Special Issue Editors

Prof. Dr. Antoni Perez-Navarro
E-Mail Website
Guest Editor
Faculty of Computer Sciences, Multimedia and Telecommunication at Universitat Oberta de Catalunya (UOC), Barcelona, Spain; Internet Interdisciplinary Institute (IN3) at UOC, Castelldefels, Spain
Interests: physics; physics and science fiction; e-learning; geographic information systems; context-aware recommender systems; location-based systems
Special Issues, Collections and Topics in MDPI journals
Dr. Raúl Montoliu
E-Mail Website
Guest Editor
Institute of new imaging technologies (INIT), Jaume I University, Castellon, Spain
Interests: indoor localization and navigation; human and social behavior from sensor data; sport video analysis and surveillance applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Locating devices in indoor environments has become a key issue for many emerging location‐based applications and intelligent spaces in different fields. However, there is no overall easy solution today. Although no large‐scale deployment of such location systems is available yet, this strategic topic is called to lead technological innovations of great impact on the daily activities of people in the coming years, in areas such as healthy and independent living, leisure, security, etc.

Topics in the area include, among others, 5G positioning; algorithms for wireless sensor networks; applications of location awareness and context detection; benchmarking, assessment, evaluation, standards, interoperability, and research reproducibility; data compression, data augmentation, and generative modeling in indoor positioning; health and wellness applications; human motion monitoring and modeling; indoor maps, indoor spatial data models, indoor mobile mapping, and 3D building models; indoor positioning, navigation, and tracking methods (such as AOA, TOF, TDOA based localization, cooperative, machine learning systems, frameworks for hybrid positioning, hybrid IMU pedestrian navigation and foot mounted navigation, magnetic field based methods, mapping, SLAM, optical systems, RFID, radar, device-free systems, routing in indoor environments, signal strength based methods, fingerprinting, ultrasound systems, UWB); privacy and security for indoor location systems; robotics and UAV; high-sensitivity GNSS, indoor GNSS, pseudolites; industrial metrology and geodetic systems; IGPS self-contained sensors; user requirements for location-based systems; visible light positioning; wearable and multisensor systems; and wireless power transfer system for localization.

Prof. Dr. Antoni Perez-Navarro
Dr. Raúl Montoliu
Dr. Joaquín Torres-Sospedra
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 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 location
  • indoor navigation
  • fingerprinting
  • UWB
  • indoor maps
  • AOA
  • TOF
  • TDOA
  • privacy

Published Papers (16 papers)

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Research

Article
Real-Time Map Matching with a Backtracking Particle Filter Using Geospatial Analysis
Sensors 2022, 22(9), 3289; https://doi.org/10.3390/s22093289 - 25 Apr 2022
Viewed by 255
Abstract
Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step [...] Read more.
Inertial odometry is a typical localization method that is widely and easily accessible in many devices. Pedestrian positioning can benefit from this approach based on inertial measurement unit (IMU) values embedded in smartphones. Fitting the inertial odometry outputs, namely step length and step heading of a human for instance, with spatial information is an ubiquitous way to correct for the cumulative noises. This so-called map-matching process can be achieved in several ways. In this paper, a novel real-time map-matching approach was developed, using a backtracking particle filter that benefits from the implemented geospatial analysis, which reduces the complexity of spatial queries and provides flexibility in the use of different kinds of spatial constraints. The goal was to generalize the algorithm to permit the use of any kind of odometry data calculated by different sensors and approaches as the input. Further research, development, and comparisons have been done by the easy implementation of different spatial constraints and use cases due to the modular structure. Additionally, a simple map-based optimization using transition areas between floors has been developed. The developed algorithm could achieve accuracies of up to 3 m at approximately the 90th percentile for two different experiments in a complex building structure. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Toward Accurate Indoor Positioning: An RSS-Based Fusion of UWB and Machine-Learning-Enhanced WiFi
Sensors 2022, 22(9), 3204; https://doi.org/10.3390/s22093204 - 21 Apr 2022
Viewed by 504
Abstract
A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work [...] Read more.
A wide variety of sensors and devices are used in indoor positioning scenarios to improve localization accuracy and overcome harsh radio propagation conditions. The availability of these individual sensors suggests the idea of sensor fusion to achieve a more accurate solution. This work aims to address, with the goal of improving localization accuracy, the fusion of two conventional candidates for indoor positioning scenarios: Ultra Wide Band (UWB) and Wireless Fidelity (WiFi). The proposed method consists of a Machine Learning (ML)-based enhancement of WiFi measurements, environment observation, and sensor fusion. In particular, the proposed algorithm takes advantage of Received Signal Strength (RSS) values to fuse range measurements utilizing a Gaussian Process (GP). The range values are calculated using the WiFi Round Trip Time (RTT) and UWB Two Way Ranging (TWR) methods. To evaluate the performance of the proposed method, trilateration is used for positioning. Furthermore, empirical range measurements are obtained to investigate and validate the proposed approach. The results prove that UWB and WiFi, working together, can compensate for each other’s limitations and, consequently, provide a more accurate position solution. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Real-Time Sonar Fusion for Layered Navigation Controller
Sensors 2022, 22(9), 3109; https://doi.org/10.3390/s22093109 - 19 Apr 2022
Viewed by 282
Abstract
Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach [...] Read more.
Navigation in varied and dynamic indoor environments remains a complex task for autonomous mobile platforms. Especially when conditions worsen, typical sensor modalities may fail to operate optimally and subsequently provide inapt input for safe navigation control. In this study, we present an approach for the navigation of a dynamic indoor environment with a mobile platform with a single or several sonar sensors using a layered control system. These sensors can operate in conditions such as rain, fog, dust, or dirt. The different control layers, such as collision avoidance and corridor following behavior, are activated based on acoustic flow queues in the fusion of the sonar images. The novelty of this work is allowing these sensors to be freely positioned on the mobile platform and providing the framework for designing the optimal navigational outcome based on a zoning system around the mobile platform. Presented in this paper is the acoustic flow model used, as well as the design of the layered controller. Next to validation in simulation, an implementation is presented and validated in a real office environment using a real mobile platform with one, two, or three sonar sensors in real time with 2D navigation. Multiple sensor layouts were validated in both the simulation and real experiments to demonstrate that the modular approach for the controller and sensor fusion works optimally. The results of this work show stable and safe navigation of indoor environments with dynamic objects. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
NIKE BLUETRACK: Blue Force Tracking in GNSS-Denied Environments Based on the Fusion of UWB, IMUs and 3D Models
Sensors 2022, 22(8), 2982; https://doi.org/10.3390/s22082982 - 13 Apr 2022
Viewed by 442
Abstract
Blue force tracking represents an essential task in the field of military applications. A blue force tracking system provides the location information of their own forces on a map to commanders. For the command post, this results in more efficient operation control with [...] Read more.
Blue force tracking represents an essential task in the field of military applications. A blue force tracking system provides the location information of their own forces on a map to commanders. For the command post, this results in more efficient operation control with increasing safety. In underground structures (e.g., tunnels or subways), the localisation is challenging due to the lack of GNSS signals. This paper presents a localisation system for military or emergency forces tailored to usage in complex underground structures. In a particle filter, position changes from a dual foot-mounted INS are fused with opportunistic UWB ranges and data from a 3D tunnel model to derive position information. A concept to deal with the absence of UWB infrastructure or 3D tunnel models is illustrated. Recurrent neural network methodologies are applied to cope with different motion types of the operators. The evaluation of the positioning algorithm took place in a street tunnel. If a fully installed infrastructure was available, positioning errors under one metre were reached. The results also showed that the INS can bridge UWB outages. A particle-filter-based approach to UWB anchor mapping is presented, and the first simulation results showed its viability. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Accuracy and Precision of Agents Orientation in an Indoor Positioning System Using Multiple Infrastructure Lighting Spotlights and a PSD Sensor
Sensors 2022, 22(8), 2882; https://doi.org/10.3390/s22082882 - 09 Apr 2022
Viewed by 430
Abstract
In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device [...] Read more.
In indoor localization there are applications in which the orientation of the agent to be located is as important as knowing the position. In this paper we present the results of the orientation estimation from a local positioning system based on position-sensitive device (PSD) sensors and the visible light emitted from the illumination of the room in which it is located. The orientation estimation will require that the PSD sensor receives signal from either 2 or 4 light sources simultaneously. As will be shown in the article, the error determining the rotation angle of the agent with the on-board sensor is less than 0.2 degrees for two emitters. On the other hand, by using 4 light sources the three Euler rotation angles are determined, with mean errors in the measurements smaller than 0.35° for the x- and y-axis and 0.16° for the z-axis. The accuracy of the measurement has been evaluated experimentally in a 2.5 m-high ceiling room over an area of 2.2 m2 using geodetic measurement tools to establish the reference ground truth values. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Multipath-Assisted Radio Sensing and State Detection for the Connected Aircraft Cabin
Sensors 2022, 22(8), 2859; https://doi.org/10.3390/s22082859 - 08 Apr 2022
Viewed by 379
Abstract
Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger [...] Read more.
Efficiency and reliable turnaround time are core features of modern aircraft transportation and key to its future sustainability. Given the connected aircraft cabin, the deployment of digitized and interconnected sensors, devices and passengers provides comprehensive state detection within the cabin. More specifically, passenger localization and occupancy detection can be monitored using location-aware communication systems, also known as wireless sensor networks. These multi-purpose communication systems serve a variety of capabilities, ranging from passenger convenience communication services, over crew member devices, to maintenance planning. In addition, radio-based sensing enables an efficient sensory basis for state monitoring; e.g., passive seat occupancy detection. Within the scope of the connected aircraft cabin, this article presents a multipath-assisted radio sensing (MARS) approach using the propagation information of transmitted signals, which are provided by the channel impulse response (CIR) of the wireless communication channel. By performing a geometrical mapping of the CIR, reflection sources are revealed, and the occupancy state can be derived. For this task, both probabilistic filtering and k-nearest neighbor classification are discussed. In order to evaluate the proposed methods, passenger occupancy detection and state detection for the future automation of passenger safety announcements and checks are addressed. Therefore, experimental measurements are performed using commercially available wideband communication devices, both in close to ideal conditions in an RF anechoic chamber and a cabin seat mockup. In both environments, a reliable radio sensing state detection was achieved. In conclusion, this paper provides a basis for the future integration of energy and spectrally efficient joint communication and sensing radio systems within the connected aircraft cabin. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
An Extended Kalman Filter for Magnetic Field SLAM Using Gaussian Process Regression
Sensors 2022, 22(8), 2833; https://doi.org/10.3390/s22082833 - 07 Apr 2022
Viewed by 399
Abstract
We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process [...] Read more.
We present a computationally efficient algorithm for using variations in the ambient magnetic field to compensate for position drift in integrated odometry measurements (dead-reckoning estimates) through simultaneous localization and mapping (SLAM). When the magnetic field map is represented with a reduced-rank Gaussian process (GP) using Laplace basis functions defined in a cubical domain, analytic expressions of the gradient of the learned magnetic field become available. An existing approach for magnetic field SLAM with reduced-rank GP regression uses a Rao-Blackwellized particle filter (RBPF). For each incoming measurement, training of the magnetic field map using an RBPF has a computational complexity per time step of O(NpNm2), where Np is the number of particles, and Nm is the number of basis functions used to approximate the Gaussian process. Contrary to the existing particle filter-based approach, we propose applying an extended Kalman filter based on the gradients of our learned magnetic field map for simultaneous localization and mapping. Our proposed algorithm only requires training a single map. It, therefore, has a computational complexity at each time step of O(Nm2). We demonstrate the workings of the extended Kalman filter for magnetic field SLAM on an open-source data set from a foot-mounted sensor and magnetic field measurements collected onboard a model ship in an indoor pool. We observe that the drift compensating abilities of our algorithm are comparable to what has previously been demonstrated for magnetic field SLAM with an RBPF. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Meaningful Test and Evaluation of Indoor Localization Systems in Semi-Controlled Environments
Sensors 2022, 22(7), 2797; https://doi.org/10.3390/s22072797 - 06 Apr 2022
Viewed by 513
Abstract
Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature [...] Read more.
Despite their enormous potential, the use of indoor localization systems (ILS) remains seldom. One reason is the lack of market transparency and stakeholders’ trust in the systems’ performance as a consequence of insufficient use of test and evaluation (T&E) methodologies. The heterogeneous nature of ILS, their influences, and their applications pose various challenges for the design of a methodology that provides meaningful results. Methodologies for building-wide testing exist, but their use is mostly limited to associated indoor localization competitions. In this work, the T&E 4iLoc Framework is proposed—a methodology for T&E of indoor localization systems in semi-controlled environments based on a system-level and black-box approach. In contrast to building-wide testing, T&E in semi-controlled environments, such as test halls, is characterized by lower costs, higher reproducibility, and better comparability of the results. The limitation of low transferability to real-world applications is addressed by an application-driven design approach. The empirical validation of the T&E 4iLoc Framework, based on the examination of a contour-based light detection and ranging (LiDAR) ILS, an ultra wideband ILS, and a camera-based ILS for the application of automated guided vehicles in warehouse operation, demonstrates the benefits of T&E with the T&E 4iLoc Framework. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Deep Learning-Based Indoor Localization Using Multi-View BLE Signal
Sensors 2022, 22(7), 2759; https://doi.org/10.3390/s22072759 - 02 Apr 2022
Viewed by 828
Abstract
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator [...] Read more.
In this paper, we present a novel Deep Neural Network-based indoor localization method that estimates the position of a Bluetooth Low Energy (BLE) transmitter (tag) by using the received signals’ characteristics at multiple Anchor Points (APs). We use the received signal strength indicator (RSSI) value and the in-phase and quadrature-phase (IQ) components of the received BLE signals at a single time instance to simultaneously estimate the angle of arrival (AoA) at all APs. Through supervised learning on simulated data, various machine learning (ML) architectures are trained to perform AoA estimation using varying subsets of anchor points. In the final stage of the system, the estimated AoA values are fed to a positioning engine which uses the least squares (LS) algorithm to estimate the position of the tag. The proposed architectures are trained and rigorously tested on several simulated room scenarios and are shown to achieve a localization accuracy of 70 cm. Moreover, the proposed systems possess generalization capabilities by being robust to modifications in the room’s content or anchors’ configuration. Additionally, some of the proposed architectures have the ability to distribute the computational load over the APs. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Indoor Positioning of Low-Cost Narrowband IoT Nodes: Evaluation of a TDoA Approach in a Retail Environment
Sensors 2022, 22(7), 2663; https://doi.org/10.3390/s22072663 - 30 Mar 2022
Viewed by 323
Abstract
The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time [...] Read more.
The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at 2.4 GHz. We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of 2.19 m is achieved. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Experimental Evaluation of IEEE 802.15.4z UWB Ranging Performance under Interference
Sensors 2022, 22(4), 1643; https://doi.org/10.3390/s22041643 - 19 Feb 2022
Viewed by 521
Abstract
The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to [...] Read more.
The rise of precise wireless localization for industrial and consumer use is continuing to challenge a significant amount of research. Recently the new ultra-wideband standard IEEE 802.15.4z was released to increase both the robustness and security of the underlying message exchanges. Due to the lack of accessible transceivers, most of the current research on this is of theoretical nature though. This work provides the first experimental evaluation of the ranging performance in realistic environments and also assesses the robustness to different sources of interference. To evaluate the individual aspects, a set of three different experiments are conducted. One experiment with realistic movement and two additional with targeted interference. It could be shown that the cryptographic additions of the new standard can provide sufficient information to improve the reliability of the ranging results under multi-user interference significantly. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Component-Wise Error Correction Method for UWB-Based Localization in Target-Following Mobile Robot
Sensors 2022, 22(3), 1180; https://doi.org/10.3390/s22031180 - 04 Feb 2022
Cited by 1 | Viewed by 521
Abstract
Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization [...] Read more.
Target-following mobile robots have gained attention in various industrial applications. This study proposes an ultra-wideband-based target localization method that provides highly accurate and robust target tracking performance for a following robot. Based on the least square approximation framework, the proposed method improves localization accuracy by compensating localization bias and high-frequency deviations component by component. Initial calibration method is proposed to measure the device-dependent localization bias, which enables a compensation of the bias error not only at the calibration points, but also at the any other points. An iterative complementary filter, which recursively produces optimal estimation for each timeframe as a weighted sum of previous and current estimation depending on the reliability of each estimation, is proposed to reduce the deviation of the localization error. The performance of the proposed method is validated using simulations and experiments. Both the magnitude and deviation of the localization error were significantly improved by up to 77 and 51%, respectively, compared with the previous method. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Can I Trust This Location Estimate? Reproducibly Benchmarking the Methods of Dynamic Accuracy Estimation of Localization
Sensors 2022, 22(3), 1088; https://doi.org/10.3390/s22031088 - 30 Jan 2022
Viewed by 896
Abstract
Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The [...] Read more.
Despite the great attention that the research community has paid to the creation of novel indoor positioning methods, a rather limited volume of works has focused on the confidence that Indoor Positioning Systems (IPS) assign to the position estimates that they produce. The concept of estimating, dynamically, the accuracy of the position estimates provided by an IPS has been sporadically studied in the literature of the field. Recently, this concept has started being studied as well in the context of outdoor positioning systems of Internet of Things (IoT) based on Low-Power Wide-Area Networks (LPWANs). What is problematic is that the consistent comparison of the proposed methods is quasi nonexistent: new methods rarely use previous ones as baselines; often, a small number of evaluation metrics are reported while different metrics are reported among different relevant publications, the use of open data is rare, and the publication of open code is absent. In this work, we present an open-source, reproducible benchmarking framework for evaluating and consistently comparing various methods of Dynamic Accuracy Estimation (DAE). This work reviews the relevant literature, presenting in a consistent terminology commonalities and differences and discussing baselines and evaluation metrics. Moreover, it evaluates multiple methods of DAE using open data, open code, and a rich set of relevant evaluation metrics. This is the first work aiming to establish the state of the art of methods of DAE determination in IPS and in LPWAN positioning systems, through an open, transparent, holistic, reproducible, and consistent evaluation of the methods proposed in the relevant literature. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Simulation Tool and Online Demonstrator for CDMA-Based Ultrasonic Indoor Localization Systems
Sensors 2022, 22(3), 1038; https://doi.org/10.3390/s22031038 - 28 Jan 2022
Viewed by 684
Abstract
This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences [...] Read more.
This work presents the CODEUS platform, which includes a simulation tool together with an online experimental demonstrator to offer analysis and testing flexibility for researchers and developers in Ultrasonic Indoor Positioning Systems (UIPSs). The simulation platform allows most common encoding techniques and sequences to be tested in a configurable UIPS. It models the signal modulation and processing, the ultrasonic transducers’ response, the beacon distribution, the channel propagation effects, the synchronism, and the application of different positioning algorithms. CODEUS provides results and performance analysis for different metrics and at different stages of the signal processing. The UIPS simulation tool is specified by means of the MATLAB© App-Designer environment, which enables the definition of a user-friendly interface. It has also been linked to an online demonstrator that can be managed remotely by means of a website, thus avoiding any hardware requirement or equipment on behalf of researchers. This demonstrator allows the selected transmission schemes, modulation or encoding techniques to be validated in a real UIPS, therefore enabling a fast and easy way of carrying out experimental tests in a laboratory environment, while avoiding the time-consuming tasks related to electronic design and prototyping in the UIPS field. Both simulator and online demonstrator are freely available for researchers and students through the corresponding website. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Using Perspective-n-Point Algorithms for a Local Positioning System Based on LEDs and a QADA Receiver
Sensors 2021, 21(19), 6537; https://doi.org/10.3390/s21196537 - 30 Sep 2021
Cited by 2 | Viewed by 727
Abstract
The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to [...] Read more.
The research interest on location-based services has increased during the last years ever since 3D centimetre accuracy inside intelligent environments could be confronted with. This work proposes an indoor local positioning system based on LED lighting, transmitted from a set of beacons to a receiver. The receiver is based on a quadrant photodiode angular diversity aperture (QADA) plus an aperture placed over it. This configuration can be modelled as a perspective camera, where the image position of the transmitters can be used to recover the receiver’s 3D pose. This process is known as the perspective-n-point (PnP) problem, which is well known in computer vision and photogrammetry. This work investigates the use of different state-of-the-art PnP algorithms to localize the receiver in a large space of 2 × 2 m2 based on four co-planar transmitters and with a distance from transmitters to receiver up to 3.4 m. Encoding techniques are used to permit the simultaneous emission of all the transmitted signals and their processing in the receiver. In addition, correlation techniques (match filtering) are used to determine the image points projected from each emitter on the QADA. This work uses Monte Carlo simulations to characterize the absolute errors for a grid of test points under noisy measurements, as well as the robustness of the system when varying the 3D location of one transmitter. The IPPE algorithm obtained the best performance in this configuration. The proposal has also been experimentally evaluated in a real setup. The estimation of the receiver’s position at three particular points for roll angles of the receiver of γ={0°, 120°, 210° and 300°} using the IPPE algorithm achieves average absolute errors and standard deviations of 4.33 cm, 3.51 cm and 28.90 cm; and 1.84 cm, 1.17 cm and 19.80 cm in the coordinates x, y and z, respectively. These positioning results are in line with those obtained in previous work using triangulation techniques but with the addition that the complete pose of the receiver (x, y, z, α, β, γ) is obtained in this proposal. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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Article
Smartphone-Based Inertial Odometry for Blind Walkers
Sensors 2021, 21(12), 4033; https://doi.org/10.3390/s21124033 - 11 Jun 2021
Viewed by 1037
Abstract
Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a [...] Read more.
Pedestrian tracking systems implemented in regular smartphones may provide a convenient mechanism for wayfinding and backtracking for people who are blind. However, virtually all existing studies only considered sighted participants, whose gait pattern may be different from that of blind walkers using a long cane or a dog guide. In this contribution, we present a comparative assessment of several algorithms using inertial sensors for pedestrian tracking, as applied to data from WeAllWalk, the only published inertial sensor dataset collected indoors from blind walkers. We consider two situations of interest. In the first situation, a map of the building is not available, in which case we assume that users walk in a network of corridors intersecting at 45° or 90°. We propose a new two-stage turn detector that, combined with an LSTM-based step counter, can robustly reconstruct the path traversed. We compare this with RoNIN, a state-of-the-art algorithm based on deep learning. In the second situation, a map is available, which provides a strong prior on the possible trajectories. For these situations, we experiment with particle filtering, with an additional clustering stage based on mean shift. Our results highlight the importance of training and testing inertial odometry systems for assisted navigation with data from blind walkers. Full article
(This article belongs to the Special Issue Advances in Indoor Positioning and Indoor Navigation)
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