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Advanced Approaches for Indoor Localization and Navigation
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Advanced Approaches for Indoor Localization and Navigation

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

Deadline for manuscript submissions: closed (30 April 2022) | Viewed by 60187

Special Issue Editor

Dr. David Plets

E-Mail Website
Guest Editor
Department of Information Technology, Ghent University, Ghent, Belgium
Interests: localization; wireless propagation; IoT; wireless networks; visible light positioning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Indoor localization has attracted enormous attention from both academia and industry, given the multitude of location-based services (LBS). These are situated in various domains, such as healthcare, government, public service, industrial, military, retail, or the cultural sector. Examples of these location-aware applications are personal navigation, museum guidance, intrusion detection, finding your car in a parking garage, wayfinding in a large shopping mall or hospital, asset tracking, fleet and inventory management, optimizing productivity in manufacturing or distribution, etc. These applications are facilitated by the growing amount of available positioning data due to ubiquitous connectivity and the Internet of Things (IoT).

Although different techniques and technologies are already quite established in the localization domain (e.g., Wi-Fi- or BLE-based RF fingerprinting and trilateration), novel evolutions have been gaining increased attention, e.g., localization based on visible light signals, the availability of AoA and ToF data to enable hybrid RF approaches, 3D UAV indoor localization techniques, machine learning approaches, etc. It is expected that these ongoing research efforts will further support a widespread adoption of LBS, thanks to a higher accuracy and precision and a lower deployment cost.

This Special Issue aims to report high-quality research in recent advances in the indoor localization and navigation domain. Topics of interest include but are not limited to those covered by the keyword list below.

Prof. Dr. David Plets
Guest Editor

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Keywords

  • RF-based localization
  • Hybrid localization techniques (RSS, AoA, ToF, etc.)
  • IMU-supported localization
  • RFID localization
  • Device-free indoor localization
  • Magnetic indoor localization
  • Machine learning techniques for indoor localization
  • Visible light positioning (VLP)
  • Novel indoor UAV and AGV navigation techniques

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Published Papers (18 papers)

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25 pages, 46086 KiB  
Article
A Rescue-Assistance Navigation Method by Using the Underground Location of WSN after Disasters
by Shuo Li, Tiancheng Guo, Ran Mo, Xiaoshuai Zhao, Feng Zhou, Weirong Liu and Jun Peng
Sensors 2020, 20(8), 2173; https://doi.org/10.3390/s20082173 - 11 Apr 2020
Cited by 6 | Viewed by 2933
Abstract
A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of [...] Read more.
A challenging rescue task for the underground disaster is to guide survivors in getting away from the dangerous area quickly. To address the issue, an escape guidance path developing method is proposed based on anisotropic underground wireless sensor networks under the condition of sparse anchor nodes. Firstly, a hybrid channel model was constructed to reflect the relationship between distance and receiving signal strength, which incorporates the underground complex communication characteristics, including the analytical ray wave guide model, the Shadowing effect, the tunnel size, and the penetration effect of obstacles. Secondly, a trustable anchor node selection algorithm with node movement detection is proposed, which solves the problem of high-precision node location in anisotropic networks with sparse anchor nodes after the disaster. Consequently, according to the node location and the obstacles, the optimal guidance path is developed by using the modified minimum spanning tree algorithm. Finally, the simulations in the 3D scene are conducted to verify the performance of the proposed method on the localization accuracy, guidance path effectiveness, and scalability. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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18 pages, 4936 KiB  
Article
Graph Optimization Model Fusing BLE Ranging with Wi-Fi Fingerprint for Indoor Positioning
by Rong Zhou, Puchun Chen, Jing Teng and Fengying Meng
Sensors 2022, 22(11), 4045; https://doi.org/10.3390/s22114045 - 26 May 2022
Viewed by 1885
Abstract
To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of g2o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the [...] Read more.
To improve the user’s positioning accuracy of a Wi-Fi fingerprint-based positioning algorithm, this study proposes a graph optimization model based on the framework of g2o that fuses a Wi-Fi fingerprint and Bluetooth Low Energy (BLE) ranging technologies. In our model, the improvement in positioning can be formulated as a nonlinear least-squares optimization problem that a graph can represent. The graph regards users as nodes and our self-designed error functions between users as edges. In the graph, the nodes obtain the initial coordinates through Wi-Fi fingerprint positioning, and all error functions aggregate to a total error function to be solved. To improve the solution effect of the total error function and weaken the influence of measurement error, an information matrix, an edge selection principle, and a Huber kernel function are introduced. The Levenberg–Marquardt (LM) algorithm is used to solve the total error function and the affine transformation estimation is used for the drifting solution. Through experiments, the influence of the threshold in the Huber kernel function is explored, the relationship between the number of nodes in the graph and the optimization effect is analyzed, and the impact of the distribution of nodes is researched. The experimental results show improvements in the positioning accuracy of four common Wi-Fi fingerprint-matching algorithms: KNN, WKNN, GK, and Stg. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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19 pages, 3558 KiB  
Article
RSS/TDoA-Based Source Localization in Microwave UWB Sensors Networks Using Two Anchor Nodes
by Sergei Ivanov, Vladimir Kuptsov, Vladimir Badenko and Alexander Fedotov
Sensors 2022, 22(8), 3018; https://doi.org/10.3390/s22083018 - 14 Apr 2022
Cited by 9 | Viewed by 2219
Abstract
The manuscript presents an algorithm for the optimal estimation of the amplitude and propagation delay time of an ultra-wideband radio signal, in systems for the passive location of fixed targets based on the hybrid RSS/TDoA method in two-dimensional space with two base stations. [...] Read more.
The manuscript presents an algorithm for the optimal estimation of the amplitude and propagation delay time of an ultra-wideband radio signal, in systems for the passive location of fixed targets based on the hybrid RSS/TDoA method in two-dimensional space with two base stations. The optimal estimate is based on the Bayesian strategy of maximum a posteriori probability density, taking into account a priori data on the statistical properties of the Line of Sight radio channel during Gaussian monocycle propagation. The Bayesian Cramer–Rao lower bound (BCRLB) of the delay time and the amplitude estimates for a time-discrete signal are calculated, and the resulting parameter estimate is compared with BCRLB. An algorithm has been developed for optimal estimation of distances from the radiation source to base stations, based on the results of the measurements of the amplitude and the propagation delay time of the UWB radio signal. The calculation of the statistical characteristics of the obtained estimate is carried out, and the functional dependence of the characteristics on various parameters is analyzed. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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16 pages, 3170 KiB  
Article
Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking
by Humayun Khan, Adrian Clark, Graeme Woodward and Robert W. Lindeman
Sensors 2020, 20(18), 5031; https://doi.org/10.3390/s20185031 - 4 Sep 2020
Cited by 3 | Viewed by 3650
Abstract
In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. [...] Read more.
In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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21 pages, 2069 KiB  
Article
Simultaneous Indoor Pedestrian Localization and House Mapping Based on Inertial Measurement Unit and Bluetooth Low-Energy Beacon Data
by Jesus D. Ceron, Felix Kluge, Arne Küderle, Bjoern M. Eskofier and Diego M. López
Sensors 2020, 20(17), 4742; https://doi.org/10.3390/s20174742 - 22 Aug 2020
Cited by 9 | Viewed by 3912
Abstract
Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with [...] Read more.
Indoor location estimation is crucial to provide context-based assistance in home environments. In this study, a method for simultaneous indoor pedestrian localization and house mapping is proposed and evaluated. The method fuses a person’s movement data from an Inertial Measurement Unit (IMU) with proximity and activity-related data from Bluetooth Low-Energy (BLE) beacons deployed in the indoor environment. The person’s and beacons’ localization is performed simultaneously using a combination of particle and Kalman Filters. We evaluated the method using data from eight participants who performed different activities in an indoor environment. As a result, the average participant’s localization error was 1.05 ± 0.44 m, and the average beacons’ localization error was 0.82 ± 0.24 m. The proposed method is able to construct a map of the indoor environment by localizing the BLE beacons and simultaneously locating the person. The results obtained demonstrate that the proposed method could point to a promising roadmap towards the development of simultaneous localization and home mapping system based only on one IMU and a few BLE beacons. To the best of our knowledge, this is the first method that includes the beacons’ data movement as activity-related events in a method for pedestrian Simultaneous Localization and Mapping (SLAM). Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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17 pages, 6146 KiB  
Article
Base Station Selection for Hybrid TDOA/RTT/DOA Positioning in Mixed LOS/NLOS Environment
by Zhongliang Deng, Hanhua Wang, Xinyu Zheng and Lu Yin
Sensors 2020, 20(15), 4132; https://doi.org/10.3390/s20154132 - 24 Jul 2020
Cited by 23 | Viewed by 3317
Abstract
The fifth generation (5G) cellular communication system is designed to support Time Difference of Arrival (TDOA), Round-Trip Time (RTT), and Direction of Arrival (DOA) measurements for indoor positioning. To mitigate the positioning error caused by non-line-of-sight (NLOS), existing base station selection methods identify [...] Read more.
The fifth generation (5G) cellular communication system is designed to support Time Difference of Arrival (TDOA), Round-Trip Time (RTT), and Direction of Arrival (DOA) measurements for indoor positioning. To mitigate the positioning error caused by non-line-of-sight (NLOS), existing base station selection methods identify channel conditions and only use line-of-sight (LOS) signals for positioning. However, different selected base station combination would lead to a different geometric dilution of precision (GDOP), base station selection based only on channel condition is not fully applicable for the hybrid positioning. This paper derives the GDOP for the hybrid TDOA, RTT, and DOA positioning, and proposes a GDOP-assisted base station selection method, which is based on both channel conditions and GDOP value changes. The simulation shows that using the proposed base station selection method could lead to higher positioning accuracy than base station selection based only on channel condition. In the simulation, in the side region of the scenario, where the change of selected base station combination causes a notable increment in GDOP value, the positioning accuracy improvement caused by the proposed method is greater than that in the center region. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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15 pages, 6268 KiB  
Article
Performance, Accuracy and Generalization Capability of RFID Tags’ Constellation for Indoor Localization
by Elias Hatem, Sara Abou-Chakra, Elizabeth Colin, Jean-Marc Laheurte and Bachar El-Hassan
Sensors 2020, 20(15), 4100; https://doi.org/10.3390/s20154100 - 23 Jul 2020
Cited by 14 | Viewed by 3544
Abstract
Indoor localization has recently witnessed an increase in interest due to its wide range of potential services. Further, the location information is very important in many applications, such as the Internet of Things, logistics, library management and so on. Hence, different technologies and [...] Read more.
Indoor localization has recently witnessed an increase in interest due to its wide range of potential services. Further, the location information is very important in many applications, such as the Internet of Things, logistics, library management and so on. Hence, different technologies and techniques have been proposed in the literature for indoor localization systems. Most of these systems present the disadvantages of a poor performance, low accuracy and high cost. However, thanks to its low cost, high accuracy and non-line-of-sight detection, radio frequency identification (RFID)-based localization has increasingly become the most used technology for indoor localization. In this paper, we propose an innovative approach based on the multiple input single output (MISO) protocol to improve the accuracy of a low-cost RFID localization system. Whereas most traditional systems use a single tag for localization, the proposed architecture encourages the use of a group of RFID tags named as a constellation. According to experimental results and based on the signals’ diversity, the location accuracy is improved to get an estimated position error of 81 cm at the cumulative distribution function of 90%. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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27 pages, 3449 KiB  
Article
A Grey Model and Mixture Gaussian Residual Analysis-Based Position Estimator in an Indoor Environment
by Yan Wang, Wenjia Ren, Long Cheng and Jijun Zou
Sensors 2020, 20(14), 3941; https://doi.org/10.3390/s20143941 - 15 Jul 2020
Cited by 9 | Viewed by 3212
Abstract
As the progress of electronics and information processing technology continues, indoor localization has become a research hotspot in wireless sensor networks (WSN). The adverse non-line of sight (NLOS) propagation usually causes large measurement errors in complex indoor environments. It could decrease the localization [...] Read more.
As the progress of electronics and information processing technology continues, indoor localization has become a research hotspot in wireless sensor networks (WSN). The adverse non-line of sight (NLOS) propagation usually causes large measurement errors in complex indoor environments. It could decrease the localization accuracy seriously. A traditional grey model considers the motion characteristics but does not take the NLOS propagation into account. A robust interacting multiple model (R-IMM) could effectively mitigate NLOS errors but the clipping point is hard to choose. In order to easily cope with NLOS errors, we present a novel filter framework: mixture Gaussian fitting-based grey Kalman filter structure (MGF-GKFS). Firstly, grey Kalman filter (GKF) is proposed to pre-process the measured distance, which can mitigate the process noise and alleviate NLOS errors. Secondly, we calculate the residual which is the difference between the filtered distance of GKF and the measured distance. Thirdly, a soft decision method based on mixture Gaussian fitting (MGF) is proposed to identify the propagation condition through residual value and give the degree of membership. Fourthly, weak NLOS noise is further processed by unscented Kalman filter (UKF). The filtered results of GKF and UKF are weighted using the degree of membership. Finally, a maximum likelihood (ML) algorithm is applied to get the coordinate of the target. MGF-GKFS is not supported by any of the priori knowledge. Full-scale simulations and an experiment are conducted to compare the localization accuracy and robustness with the state-of-the-art algorithms, including robust interacting multiple model (R-IMM), unscented Kalman filter (UKF) and interacting multiple model (IMM). The results show that MGF-GKFS could achieve significant improvement compared to R-IMM, UKF and IMM algorithms. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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14 pages, 4495 KiB  
Article
Indoor Localization Based on VIO System and Three-Dimensional Map Matching
by Jitong Zhang, Mingrong Ren, Pu Wang, Juan Meng and Yuman Mu
Sensors 2020, 20(10), 2790; https://doi.org/10.3390/s20102790 - 14 May 2020
Cited by 14 | Viewed by 3190
Abstract
High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we [...] Read more.
High-precision indoor localization plays a vital role in various places. In recent years, visual inertial odometry (VIO) system has achieved outstanding progress in the field of indoor localization. However, it is easily affected by poor lighting and featureless environments. For this problem, we propose an indoor localization algorithm based on VIO system and three-dimensional (3D) map matching. The 3D map matching is to add height matching on the basis of previous two-dimensional (2D) matching so that the algorithm has more universal applicability. Firstly, the conditional random field model is established. Secondly, an indoor three-dimensional digital map is used as a priori information. Thirdly, the pose and position information output by the VIO system are used as the observation information of the conditional random field (CRF). Finally, the optimal states sequence is obtained and employed as the feedback information to correct the trajectory of VIO system. Experimental results show that our algorithm can effectively improve the positioning accuracy of VIO system in the indoor area of poor lighting and featureless. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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24 pages, 4285 KiB  
Article
Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information
by Ruofei Gao, Jie Zhang, Wendong Xiao and Yanjiao Li
Sensors 2019, 19(21), 4783; https://doi.org/10.3390/s19214783 - 3 Nov 2019
Cited by 7 | Viewed by 3176
Abstract
Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices [...] Read more.
Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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17 pages, 1830 KiB  
Article
Semantic Localization System for Robots at Large Indoor Environments Based on Environmental Stimuli
by Fco-Javier Serrano, Vidal Moreno, Belén Curto and Raul Álves
Sensors 2020, 20(7), 2116; https://doi.org/10.3390/s20072116 - 9 Apr 2020
Cited by 3 | Viewed by 2663
Abstract
In this paper, we present a new procedure to solve the global localization of mobile robots called Environmental Stimulus Localization (ESL). We propose that the presence of common facts on the environment around the robot can be considered as stimuli for the procedure. [...] Read more.
In this paper, we present a new procedure to solve the global localization of mobile robots called Environmental Stimulus Localization (ESL). We propose that the presence of common facts on the environment around the robot can be considered as stimuli for the procedure. The robust performance of our approach is supported by two concurrent particle filters. A primary particle filter estimates and tracks the robot position, while a secondary filter is fired by environmental stimuli, helps to reduce the influence of measurement errors and allows an earlier recovery from localization failures. We have successfully used this method in a 5000 m 2 real indoor environment using as inputs the available environment information from a Geographical Information System (GIS) map, the robot’s odometry and the output of an algorithm for the perception of facts from the environment. We present a case study and the result of different tests, showing the performance of our method under the influence of errors in real applications. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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17 pages, 2523 KiB  
Article
Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes
by Piotr Rajchowski, Jacek Stefanski, Jaroslaw Sadowski and Krzysztof K. Cwalina
Sensors 2020, 20(7), 1984; https://doi.org/10.3390/s20071984 - 2 Apr 2020
Cited by 2 | Viewed by 2450
Abstract
In this article a novel method of positional data integration in an indoor hybrid localization system combining inertial navigation with radio distance measurements is presented. A point of interest is the situation when the positional data and the radio distance measurements are obtained [...] Read more.
In this article a novel method of positional data integration in an indoor hybrid localization system combining inertial navigation with radio distance measurements is presented. A point of interest is the situation when the positional data and the radio distance measurements are obtained from less than thee reference nodes and it is impossible to unambiguously localize the moving person due to undetermined set of positional equations. The presented method allows to continuously provide localization service even in areas with disturbed propagation of the radio signals. Authors performed simulation and measurement studies of the proposed method to verify the precision of position estimation of a moving person in an indoor environment. It is worth noting that to determine the simulation parameters and realize the experimental studies the hybrid localization system demonstrator was developed, combining inertial navigation and radio distance measurements. In the proposed solution, results of distance measurements taken to less than three reference nodes are used to compensate the drift of the position estimated using the inertial sensor. In the obtained simulation and experimental results it was possible to reduce the localization error by nearly 50% regarding the case when only inertial navigation was used, additionally keeping the long term root mean square error at the level of ca. 0.50 m. That gives a degradation of localization precision below 0.1 m with respect to the fusion Kalman filtration when four reference nodes are present. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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19 pages, 3283 KiB  
Article
Passive Visible Light Detection of Humans
by Kenneth Deprez, Sander Bastiaens, Luc Martens, Wout Joseph and David Plets
Sensors 2020, 20(7), 1902; https://doi.org/10.3390/s20071902 - 29 Mar 2020
Cited by 14 | Viewed by 3451
Abstract
This paper experimentally investigates passive human visible light sensing (VLS). A passive VLS system is tested consisting of one light emitting diode (LED) and one photodiode-based receiver, both ceiling-mounted. There is no line of sight between the LED and the receiver, so only [...] Read more.
This paper experimentally investigates passive human visible light sensing (VLS). A passive VLS system is tested consisting of one light emitting diode (LED) and one photodiode-based receiver, both ceiling-mounted. There is no line of sight between the LED and the receiver, so only reflected light can be considered. The influence of a human is investigated based on the received signal strength (RSS) values of the reflections of ambient light at the photodiode. Depending on the situation, this influence can reach up to ± 50 % . The experimental results show the influence of three various clothing colors, four different walking directions and four different layouts. Based on the obtained results, a human pass-by detection system is proposed and tested. The system achieves a detection rate of 100% in a controlled environment for 21 experiments. For a realistic corridor experiment, the system keeps its detection rate of 100% for 19 experiments. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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26 pages, 8246 KiB  
Article
Adaptive Indoor Area Localization for Perpetual Crowdsourced Data Collection
by Marius Laska, Jörg Blankenbach and Ralf Klamma
Sensors 2020, 20(5), 1443; https://doi.org/10.3390/s20051443 - 6 Mar 2020
Cited by 10 | Viewed by 2970
Abstract
The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes [...] Read more.
The accuracy of fingerprinting-based indoor localization correlates with the quality and up-to-dateness of collected training data. Perpetual crowdsourced data collection reduces manual labeling effort and provides a fresh data base. However, the decentralized collection comes with the cost of heterogeneous data that causes performance degradation. In settings with imperfect data, area localization can provide higher positioning guarantees than exact position estimation. Existing area localization solutions employ a static segmentation into areas that is independent of the available training data. This approach is not applicable for crowdsoucred data collection, which features an unbalanced spatial training data distribution that evolves over time. A segmentation is required that utilizes the existing training data distribution and adapts once new data is accumulated. We propose an algorithm for data-aware floor plan segmentation and a selection metric that balances expressiveness (information gain) and performance (correctly classified examples) of area classifiers. We utilize supervised machine learning, in particular, deep learning, to train the area classifiers. We demonstrate how to regularly provide an area localization model that adapts its prediction space to the accumulating training data. The resulting models are shown to provide higher reliability compared to models that pinpoint the exact position. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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25 pages, 8687 KiB  
Article
A Vision-Based Odometer for Localization of Omnidirectional Indoor Robots
by Cosimo Patruno, Roberto Colella, Massimiliano Nitti, Vito Renò, Nicola Mosca and Ettore Stella
Sensors 2020, 20(3), 875; https://doi.org/10.3390/s20030875 - 6 Feb 2020
Cited by 23 | Viewed by 4620
Abstract
In this paper we tackle the problem of indoor robot localization by using a vision-based approach. Specifically, we propose a visual odometer able to give back the relative pose of an omnidirectional automatic guided vehicle (AGV) that moves inside an indoor industrial environment. [...] Read more.
In this paper we tackle the problem of indoor robot localization by using a vision-based approach. Specifically, we propose a visual odometer able to give back the relative pose of an omnidirectional automatic guided vehicle (AGV) that moves inside an indoor industrial environment. A monocular downward-looking camera having the optical axis nearly perpendicular to the ground floor, is used for collecting floor images. After a preliminary analysis of images aimed at detecting robust point features (keypoints) takes place, specific descriptors associated to the keypoints enable to match the detected points to their consecutive frames. A robust correspondence feature filter based on statistical and geometrical information is devised for rejecting those incorrect matchings, thus delivering better pose estimations. A camera pose compensation is further introduced for ensuring better positioning accuracy. The effectiveness of proposed methodology has been proven through several experiments, in laboratory as well as in an industrial setting. Both quantitative and qualitative evaluations have been made. Outcomes have shown that the method provides a final positioning percentage error of 0.21% on an average distance of 17.2 m. A longer run in an industrial context has provided comparable results (a percentage error of 0.94% after about 80 m). The average relative positioning error is about 3%, which is still in good agreement with current state of the art. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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13 pages, 3421 KiB  
Article
Improving Positioning Accuracy via Map Matching Algorithm for Visual–Inertial Odometer
by Juan Meng, Mingrong Ren, Pu Wang, Jitong Zhang and Yuman Mou
Sensors 2020, 20(2), 552; https://doi.org/10.3390/s20020552 - 19 Jan 2020
Cited by 12 | Viewed by 3695
Abstract
A visual–inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination [...] Read more.
A visual–inertial odometer is used to fuse the image information obtained by a vision sensor with the data measured by an inertial sensor and recover the motion track online in a global frame. However, in an indoor environment, geometric transformation, sparse features, illumination changes, blurring, and noise will occur, which will either cause a reduction in or failure of the positioning accuracy. To solve this problem, a map matching algorithm based on an indoor plane structure map is proposed to improve the positioning accuracy of the system; this algorithm was implemented using a conditional random field model. The output of the attitude information from the visual–inertial odometer was used as the input of the conditional random field model. The feature function between the attitude information and the expected value was established, and the maximum probabilistic value of the attitude was estimated. Finally, the closed-loop feedback correction of the visual–inertial system was carried out with the probabilistic attitude value. A number of experiments were designed to verify the feasibility and reliability of the positioning method proposed in this paper. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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27 pages, 6606 KiB  
Article
Accurate Indoor-Positioning Model Based on People Effect and Ray-Tracing Propagation
by Firdaus Firdaus, Noor Azurati Ahmad and Shamsul Sahibuddin
Sensors 2019, 19(24), 5546; https://doi.org/10.3390/s19245546 - 15 Dec 2019
Cited by 14 | Viewed by 3913
Abstract
Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled [...] Read more.
Wireless local area networks (WLAN)-fingerprinting has been highlighted as the preferred technology for indoor positioning due to its accurate positioning and minimal infrastructure cost. However, its accuracy is highly influenced by obstacles that cause fluctuation in the signal strength. Many researchers have modeled static obstacles such as walls and ceilings, but few studies have modeled the people’s presence effect (PPE), although the human body has a great impact on signal strength. Therefore, PPE must be addressed to obtain accurate positioning results. Previous research has proposed a model to address this issue, but these studies only considered the direct path signal between the transmitter and the receiver whereas multipath effects such as reflection also have a significant influence on indoor signal propagation. This research proposes an accurate indoor-positioning model by considering people’s presence and multipath using ray-tracing, we call it (AIRY). This study proposed two solutions to construct AIRY: an automatic radio map using ray tracing and a constant of people’s effect for the received signal strength indicator (RSSI) adaptation. The proposed model was simulated using MATLAB software and tested at Level 3, Menara Razak, Universiti Teknologi Malaysia. A K-nearest-neighbor (KNN) algorithm was used to define a position. The initial accuracy was 2.04 m, which then reduced to 0.57 m after people’s presence and multipath effects were considered. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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13 pages, 4779 KiB  
Article
BLE Fingerprint Indoor Localization Algorithm Based on Eight-Neighborhood Template Matching
by Mingfeng Li, Lichen Zhao, Ding Tan and Xiaozhe Tong
Sensors 2019, 19(22), 4859; https://doi.org/10.3390/s19224859 - 7 Nov 2019
Cited by 31 | Viewed by 3853
Abstract
Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference [...] Read more.
Aiming at the problem of indoor environment, signal non-line-of-sight propagation and other factors affect the accuracy of indoor locating, an algorithm of indoor fingerprint localization based on the eight-neighborhood template is proposed. Based on the analysis of the signal strength of adjacent reference points in the fingerprint database, the methods for the eight-neighborhood template matching and generation were studied. In this study, the indoor environment was divided into four quadrants for each access point and the expected values of the received signal strength indication (RSSI) difference between the center points and their eight-neighborhoods in different quadrants were chosen as the generation parameters. Then different templates were generated for different access points, and the unknown point was located by the Euclidean distance for the correlation of RSSI between each template and its coverage area in the fingerprint database. With the spatial correlation of fingerprint data taken into account, the influence of abnormal fingerprint on locating accuracy is reduced. The experimental results show that the locating error is 1.0 m, which is about 0.2 m less than both K-nearest neighbor (KNN) and weighted K-nearest neighbor (WKNN) algorithms. Full article
(This article belongs to the Special Issue Advanced Approaches for Indoor Localization and Navigation)
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