Location estimation in smart homes setting with RFID systems

Indoor localisation technologies are a core component of Smart Homes. Many applications within Smart Homes benefit from localisation technologies to determine the locations of things, objects and people. The tremendous characteristics of the Radio Frequency Identification (RFID) systems have become one of the enabler technologies in the Internet of Things (IOT) that connect objects and things wirelessly. RFID is a promising technology in indoor positioning that not only uniquely identifies entities but also locates affixed RFID tags on objects or subjects in stationary and real-time. The rapid advancement in RFID-based systems has sparked the interest of researchers in Smart Homes to employ RFID technologies and potentials to assist with optimising (non-) pervasive healthcare systems in automated homes. In this research localisation techniques and enabled positioning sensors are investigated. Passive RFID sensors are used to localise passive tags that are affixed to Smart Home objects and track the movement of individuals in stationary and real-time settings. In this study, we develop an affordable passive localisation platform using inexpensive passive RFID sensors. To fillful this aim, a passive localisation framework using minimum tracking resources (RFID sensors) has been designed. A localisation prototype and localisation application that examined the affixed RFID tag on objects to evaluate our proposed locaisation framework was then developed. Localising algorithms were utilised to achieve enhanced accuracy of localising one particular passive tag which that affixed to target objects. This thesis uses a general enough approach so that it could be applied more widely to other applications in addition to Health Smart Homes. A passive RFID localising framework is designed and developed through systematic procedures. A localising platform is built to test the proposed framework, along with developing a RFID tracking application using Java programming language and further data analysis in MATLAB. This project applies localisation procedures and evaluates them experimentally. The experimental study positively confirms that our proposed localisation framework is capable of enhancing the accuracy of the location of the tracked individual. The low-cost design uses only one passive RFID target tag, one RFID reader and three to four antennas

Sensors and Systems for Indoor Positioning

This reprint is a reprint of the articles that appeared in Sensors' (MDPI) Special Issue on “Sensors and Systems for Indoor Positioning". The published original contributions focused on systems and technologies to enable indoor applications

Mapping, Path Following, and Perception with Long Range Passive UHF RFID for Mobile Robots

Service robots have shown an impressive potential in providing assistance and guidance in various environments, such as supermarkets, shopping malls, homes, airports, and libraries. Due to the low-cost and contactless way of communication, radio-frequency identification (RFID) technology provides a solution to overcome the difficulties (e.g. occlusions) that the traditional line of sight sensors (e.g. cameras and laser range finders) face. In this thesis, we address the applications of using passive ultra high frequency (UHF) RFID as a sensing technology for mobile robots in three fundamental tasks, namely mapping, path following, and tracking. An important task in the field of RFID is mapping, which aims at inferring the positions of RFID tags based on the measurements (i.e. the detections as well as the received signal strength) received by the RFID reader. The robot, which serves as an intelligent mobile carrier, is able to localize itself in a known environment based on the existing positioning techniques, such as laser-based Monte Carlo localization. The mapping process requires a probabilistic sensor model, which characterizes the likelihood of receiving a measurement, given the relative pose of the antenna and the tag. In this thesis, we address the problem of recovering from mapping failures of static RFID tags and localizing non-static RFID tags which do not move frequently using a particle filter. The usefulness of negative information (e.g. non-detections) is also examined in the context of mapping RFID tags. Moreover, we present a novel three dimensional (3D) sensor model to improve the mapping accuracy of RFID tags. In particular, using this new sensor model, we are able to localize the 3D position of an RFID tag by mounting two antennas at different heights on the robot. We additionally utilize negative information to improve the mapping accuracy, especially for the height estimation in our stereo antenna configuration. The model-based localization approach, which works as a dual to the mapping process, estimates the pose of the robot based on the sensor model as well as the given positions of RFID tags. The fingerprinting-based approach was shown to be superior to the model-based approach, since it is able to better capture the unpredictable radio frequency characteristics in the existing infrastructure. Here, we present a novel approach that combines RFID fingerprints and odometry information as an input of the motion control of a mobile robot for the purpose of path following in unknown environments. More precisely, we apply the teaching and playback scheme to perform this task. During the teaching stage, the robot is manually steered to move along a desired path. RFID measurements and the associated motion information are recorded in an online-fashion as reference data. In the second stage (i.e. playback stage), the robot follows this path autonomously by adjusting its pose according to the difference between the current RFIDmeasurements and the previously recorded reference measurements. Particularly, our approach needs no prior information about the distribution and positions of the tags, nor does it require a map of the environment. The proposed approach features a cost-effective alternative for mobile robot navigation if the robot is equipped with an RFID reader for inventory in RFID-tagged environments. The capability of a mobile robot to track dynamic objects is vital for efficiently interacting with its environment. Although a large number of researchers focus on the mapping of RFID tags, most of them only assume a static configuration of RFID tags and too little attention has been paid to dynamic ones. Therefore, we address the problem of tracking dynamic objects for mobile robots using RFID tags. In contrast to mapping of RFID tags, which aims at achieving a minimum mapping error, tracking does not only need a robust tracking performance, but also requires a fast reaction to the movement of the objects. To achieve this, we combine a two stage dynamic motion model with the dual particle filter, to capture the dynamic motion of the object and to quickly recover from failures in tracking. The state estimation from the particle filter is used in a combination with the VFH+ (Vector Field Histogram), which serves as a local path planner for obstacle avoidance, to guide the robot towards the target. This is then integrated into a framework, which allows the robot to search for both static and dynamic tags, follow it, and maintain the distance between them. [untranslated]Service-Roboter bergen ein großes Potential bei der Unterstützung, Beratung und Führung von Kunden oder Personal in verschiedenen Umgebungen wie zum Beispiel Supermärkten, Einkaufszentren, Wohnungen, Flughäfen und Bibliotheken. Durch die geringen Kosten und die kontaktlose Kommunikation ist die RFID Technologie in der Lage vorhandene Herausforderungen traditioneller sichtlinienbasierter Sensoren (z.B. Verdeckung beim Einsatz von Kameras oder Laser-Entfernungsmessern) zu lösen. In dieser Arbeit beschäftigen wir uns mit dem Einsatz von passivem Ultrahochfrequenz (UHF) RFID als Sensortechnologie für mobile Roboter hinsichtlich drei grundlegender Aufgabenstellungen Kartierung, Pfadverfolgung und Tracking. Kartierung nimmt eine wesentliche Rolle im Bereich der Robotik als auch beim Einsatz von RFID Sensoren ein. Hierbei ist das Ziel die Positionen von RFID-Tags anhand von Messungen (die Erfassung der Tags als solche und die Signalstärke) zu schätzen. Der Roboter, der als intelligenter mobiler Träger dient, ist in der Lage, sich selbst in einer bekannten Umgebung auf Grundlage der bestehenden Positionierungsverfahren, wie Laser-basierter Monte-Carlo Lokalisierung zurechtzufinden. Der Kartierungsprozess erfordert ein probabilistisches Sensormodell, das die Wahrscheinlichkeit beschreibt, ein Tag an einer gegebenen Position relativ zur RFID-Antenne (ggf. mit einer bestimmten Signalstärke) zu erkennen. Zentrale Aspekte dieser Arbeit sind die Regeneration bei fehlerhafter Kartierung statischer RFID-Tags und die Lokalisierung von nicht-statischen RFID-Tags. Auch wird die Verwendbarkeit negativer Informationen, wie z.B. das Nichterkennen von Transpondern, im Rahmen der RFID Kartierung untersucht. Darüber hinaus schlagen wir ein neues 3D-Sensormodell vor, welches die Genauigkeit der Kartierung von RFID-Tags verbessert. Durch die Montage von zwei Antennen auf verschiedenen Höhen des eingesetzten Roboters, erlaubt es dieses Modell im Besonderen, die 3D Positionen von Tags zu bestimmen. Dabei nutzen wir zusätzlich negative Informationen um die Genauigkeit der Kartierung zu erhöhen. Dank der Eindeutigkeit von RFID-Tags, ist es möglich die Lokalisierung eines mobilen Roboters ohne Mehrdeutigkeit zu bestimmen. Der modellbasierte Ansatz zur Lokalisierung schätzt die Pose des Roboters auf Basis des Sensormodells und den angegebenen Positionen der RFID-Tags. Es wurde gezeigt, dass der Fingerprinting-Ansatz dem modellbasierten Ansatz überlegen ist, da ersterer in der Lage ist, die unvorhersehbaren Funkfrequenzeigenschaften in der vorhandenen Infrastruktur zu erfassen. Hierfür präsentieren wir einen neuen Ansatz, der RFID Fingerprints und Odometrieinformationen für die Zwecke der Pfadverfolgung in unbekannten Umgebungen kombiniert. Dieser basiert auf dem Teaching-and-Playback-Schema. Während der Teaching-Phase wird der Roboter manuell gelenkt, um ihn entlang eines gewünschten Pfades zu bewegen. RFID-Messungen und die damit verbundenen Bewegungsinformationen werden als Referenzdaten aufgezeichnet. In der zweiten Phase, der Playback-Phase, folgt der Roboter diesem Pfad autonom. Der vorgeschlagene Ansatz bietet eine kostengünstige Alternative für die mobile Roboternavigation bei der Bestandsaufnahme in RFID-gekennzeichneten Umgebungen, wenn der Roboter mit einem RFID-Lesegerät ausgestattet ist. Die Fähigkeit eines mobilen Roboters dynamische Objekte zu verfolgen ist entscheidend für eine effiziente Interaktion mit der Umgebung. Obwohl sich viele Forscher mit der Kartierung von RFID-Tags befassen, nehmen die meisten eine statische Konfiguration der RFID-Tags an, nur wenige berücksichtigen dabei dynamische RFID-Tags. Wir wenden uns daher dem Problem der RFID basierten Verfolgung dynamischer Objekte mit mobilen Robotern zu. Im Gegensatz zur Kartierung von RFID-Tags, ist für die Verfolgung nicht nur eine stabile Erkennung notwendig, es ist zudem erforderlich schnell auf die Bewegung der Objekte reagieren zu können. Um dies zu erreichen, kombinieren wir ein zweistufiges dynamisches Bewegungsmodell mit einem dual-Partikelfilter. Die Zustandsschätzung des Partikelfilters wird in Kombination mit dem VFH+ (Vektorfeld Histogramm) verwendet, um den Roboter in Richtung des Ziels zu leiten. Hierdurch ist es dem Roboter möglich nach statischen und dynamischen Tags zu suchen, ihnen zu folgen und dabei einen gewissen Abstand zu halten

Passive RFID Rotation Dimension Reduction via Aggregation

Radio Frequency IDentification (RFID) has applications in object identification, position, and orientation tracking. RFID technology can be applied in hospitals for patient and equipment tracking, stores and warehouses for product tracking, robots for self-localisation, tracking hazardous materials, or locating any other desired object. Efficient and accurate algorithms that perform localisation are required to extract meaningful data beyond simple identification. A Received Signal Strength Indicator (RSSI) is the strength of a received radio frequency signal used to localise passive and active RFID tags. Many factors affect RSSI such as reflections, tag rotation in 3D space, and obstacles blocking line-of-sight. LANDMARC is a statistical method for estimating tag location based on a target tag’s similarity to surrounding reference tags. LANDMARC does not take into account the rotation of the target tag. By either aggregating multiple reference tag positions at various rotations, or by determining a rotation value for a newly read tag, we can perform an expected value calculation based on a comparison to the k-most similar training samples via an algorithm called K-Nearest Neighbours (KNN) more accurately. By choosing the average as the aggregation function, we improve the relative accuracy of single-rotation LANDMARC localisation by 10%, and any-rotation localisation by 20%

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

A low-cost collaborative location scheme with GNSS and RFID for the Internet of Things

The emergence and development of the Internet of Things (IoT) has attracted growing attention to low-cost location systems when facing the dramatically increased number of public infrastructure assets in smart cities. Various radio frequency identification (RFID)-based locating systems have been developed. However, most of them are impractical for infrastructure asset inspection and management on a large scale due to their high cost, inefficient deployment, and complex environments such as emergencies or high-rise buildings. In this paper, we proposed a novel locating system by combing the Global Navigation Satellite System (GNSS) with RFID, in which a target tag was located with one RFID reader and one GNSS receiver with sufficient accuracy for infrastructure asset management. To overcome the cost challenge, one mobile RFID reader-mounted GNSS receiver is used to simulate multiple location known reference tags. A vast number of reference tags are necessary for current RFID-based locating systems, which means higher cost. To achieve fine-grained location accuracy, we utilize a distance-based power law weight algorithm to estimate the exact coordinates. Our experiment demonstrates the effectiveness and advantages of the proposed scheme with sufficient accuracy, low cost and easy deployment on a large scale. The proposed scheme has potential applications for location-based services in smart cities

Localisation and navigation in GPS-denied environments using RFID tags

Includes bibliographical references.This dissertation addresses the autonomous localisation and navigation problem in the context of an underground mining environment. This kind of environment has little or no features as well as no access to GPS or stationary towers, which are usually used for navigation. In addition dust and debris may hinder optical methods for ranging. This study looks at the feasibility of using randomly distributed RFID tags to autonomously navigate in this environment. Clustering of observed tags are used for localisation, subsequently value iteration is used to navigate to a defined goal. Results are presented, concluding that it is feasible to localise and navigate using only RFID tags, in simulation. Localisation feasibility is also confirmed by experimental measurements