Indoor Positioning and Navigation

In recent years, rapid development in robotics, mobile, and communication technologies has encouraged many studies in the field of localization and navigation in indoor environments. An accurate localization system that can operate in an indoor environment has considerable practical value, because it can be built into autonomous mobile systems or a personal navigation system on a smartphone for guiding people through airports, shopping malls, museums and other public institutions, etc. Such a system would be particularly useful for blind people. Modern smartphones are equipped with numerous sensors (such as inertial sensors, cameras, and barometers) and communication modules (such as WiFi, Bluetooth, NFC, LTE/5G, and UWB capabilities), which enable the implementation of various localization algorithms, namely, visual localization, inertial navigation system, and radio localization. For the mapping of indoor environments and localization of autonomous mobile sysems, LIDAR sensors are also frequently used in addition to smartphone sensors. Visual localization and inertial navigation systems are sensitive to external disturbances; therefore, sensor fusion approaches can be used for the implementation of robust localization algorithms. These have to be optimized in order to be computationally efficient, which is essential for real-time processing and low energy consumption on a smartphone or robot

Development of a WiFi and RFID based indoor location and mobility tracking system

Ubiquitous positioning and people mobility tracking has become one of the critical parts of our daily life. As a core element of the Location Based Services (LBS), the ubiquitous positioning capability necessitates seamless positioning across both indoor and outdoor environments. Nowadays, tracking outdoor with a relatively high accuracy and reliability can be achieved using matured technologies such as Global Navigation Satellite Systems (GNSS). However, it is still challenging for tracking in indoor environments such as airports, shopping malls and museums. The demand for indoor tracking has driven the fast development of indoor positioning and tracking technologies, especially Wi-Fi, RFID and smartphone etc. All these technologies have significantly enhanced the convenience of people’s daily life and the competitiveness of business firms. With the rapidly increased ubiquity of Wi-Fi enabled mobile phones and tablets, developing a robust location and mobility tracking system utilising such technologies will have a great potential for industry innovation and applications. This research is part of an Australian Research Council (ARC) project that involves two universities and one industry partner who is a large global shopping mall management company located in Australia. The project aims to develop a smart system for robust modelling and analysing the shopping behaviours of customers so that value-added services can be effectively provided. A number of field tests have been conducted and a large amount of data has been acquired both in the shopping mall of interest and the RMIT Indoor Positioning Laboratory. A large cohort of real users in the shopping mall were recorded where only one Wi-Fi access point (AP) connection at a time for each mobile device user was provided for our research. This makes most of the conventional tracking and positioning methods inapplicable. To overcome this constraint, a new hybrid system for positioning and mobility tracking — called single AP-connection location tracking system (SCLTS) was developed, which utilised Wi-Fi, RFID and mobile device technologies and took advantage of both the cell of origin (CoO) and fingerprinting positioning methods. Three new algorithms for Wi-Fi based indoor positioning were developed during this research. They are the common handover point determination (CHOPD) algorithm for determining the boundary of the cell; the algorithm for positioning with the case of same-line-dual-connection (SLDC) in a long narrow space (e.g., a long corridor) and the algorithm for positioning with the case of perpendicular-dual-connection of APs in a T-shape corridor for improving the positioning accuracy. The architecture of the SCLTS system was also developed as part of the implementation of the SCLTS system. Various experiments were conducted in a simulated large shopping-mall-like environment (i.e., the RMIT Indoor Positioning Lab) and the results showed that the performance of the SCLTS developed was very promising and the original goal of the project has been achieved. In addition, the two most popular indoor positioning methods — trilateration and fingerprinting were also optimised and implemented in a real industrial product and promising results have been achieved

New Approach of Indoor and Outdoor Localization Systems

Accurate determination of the mobile position constitutes the basis of many new applications. This book provides a detailed account of wireless systems for positioning, signal processing, radio localization techniques (Time Difference Of Arrival), performances evaluation, and localization applications. The first section is dedicated to Satellite systems for positioning like GPS, GNSS. The second section addresses the localization applications using the wireless sensor networks. Some techniques are introduced for localization systems, especially for indoor positioning, such as Ultra Wide Band (UWB), WIFI. The last section is dedicated to Coupled GPS and other sensors. Some results of simulations, implementation and tests are given to help readers grasp the presented techniques. This is an ideal book for students, PhD students, academics and engineers in the field of Communication, localization & Signal Processing, especially in indoor and outdoor localization domains

Excavator Pose Estimation for Safety Monitoring by Fusing Computer Vision and RTLS Data

The construction industry is considered as a hazardous industry because of its high number of accidents and fatality rates. Safety is one of the main requirements on construction sites since an insecure site drops the morale of the workers, which can also result in lower productivity. To address safety issues, many proactive methods have been introduced by researchers and equipment manufacturers. Studying these methods shows that most of them are using radio-based technologies that perform based on the locations of the attached sensors to the moving objects, which could be expensive and impractical for the large fleet of available construction equipment. Safety monitoring is a sensitive task and avoiding collisions requires a detailed information of the articulated equipment (e.g. excavators) and the motion of each part of that equipment. Therefore, it is necessary to install the location sensors on each moving part of the equipment for estimating its pose, which is a difficult, time consuming, and expensive task. On the other hand, the application of Computer Vision (CV) techniques is growing and becoming more practical and affordable. However, most of the available CV-based techniques evaluate the proximity of the resources by considering each object as a single point regardless of its shape and pose. Moreover, the process of manually collecting and annotating a large image dataset of different pieces of equipment is one of the most time consuming tasks. Furthermore, relying on a single source of data may not only decrease the accuracy of the pose estimation system because of missing data or calculation errors, but it may also increase the computation time. Moreover, when there are multiple objects and equipment in the field of view of each camera, CV-based algorithms are under a higher risk of false recognition of the equipment and their parts. Therefore, fusing the cameras’ data with data from Real-Time Location System (RTLS) can help the pose estimation system by limiting the search area for the parts’ detectors, and consequently reducing the processing time and improving the accuracy by reducing the false detections. This research aims to estimate the excavator pose by fusing CV and RTLS data for safety monitoring and has the following objectives: (1) improving the CV training by developing a method to automatically generate and annotate around-view synthetic images of equipment and their parts using the 3D model of the equipment and the real images of the construction sites as background; (2) developing a guideline for applying stereo vision system in construction sites using regular surveillance cameras with long baseline at a high level; (3) improving the accuracy and speed of CV detection by fusing RTLS data with cameras’ data; and (4) estimating the 3D pose of the equipment for detecting potential collisions based on a pair of Two Dimensional (2D) skeletons of the parts from the views of two cameras. To support these objectives, a comprehensive database of the synthetic images of the excavator and its parts are generated, and multiple detectors from multiple views are trained for each part of the excavator using the image database. Moreover, the RTLS data, providing the location of the equipment, are linked with the corresponding video frames from two cameras to fuse the location data with the video data. Knowing the overall size of the equipment and its location provided by the RTLS system, a virtual cylinder defined around the equipment is projected on the video frames to limit the search scope of the object detection algorithm within the projected cylinder, resulting in a faster processing time and higher detection accuracy. Additionally, knowing the equipment ID assigned to each RTLS device and the cameras’ locations and heights, it is possible to select the suitable detectors for each equipment. After detecting a part, the background of the detected bounding box are removed to estimate the location and orientation of each part. The final skeleton of the excavator is derived by connecting the start and end points of the parts to their adjacent parts knowing the kinematic information of the excavator. Estimating the skeleton of the excavator from each camera view on one hand, and knowing the extrinsic and intrinsic parameters of all available cameras on the construction site, on the other hand, are used for estimating the 3D pose by triangulating the estimated skeleton from each camera. In order to use the available collision avoidance systems, the 3D pose of the excavator is sent to the game environment and the potential collisions are detected followed by generating a warning. The contributions of this research are: (1) developing a method for creating and annotating the synthetic images of the construction equipment and their parts using the equipment 3D models and the real images of the construction sites; (2) creating and training the HOG-based excavator’s parts detectors using the database of the synthetic images developed earlier and automatically produced negative samples from the other excavator parts in addition to the real images of different construction sites while the target object is cut from these; (3) developing a data fusion framework after calibrating two regular surveillance cameras with the long baseline to integrate the RTLS data received from GPS with the video data from the cameras to decrease the processing efforts for detecting excavator parts while increasing the detection accuracy by limiting the search scope for the detectors; (4) developing a clustering technique to subtract parts’ background and extracting the 2D skeleton of the excavator in each camera’s view and to estimate the 3D pose of the excavator; and (5) transferring the 3D pose data of the excavator to the game environment using TCP/IP connection and visualizing the near real-time pose of the excavator in the game engine for detecting the potential collisions

Recent Advances in Indoor Localization Systems and Technologies

Despite the enormous technical progress seen in the past few years, the maturity of indoor localization technologies has not yet reached the level of GNSS solutions. The 23 selected papers in this book present the recent advances and new developments in indoor localization systems and technologies, propose novel or improved methods with increased performance, provide insight into various aspects of quality control, and also introduce some unorthodox positioning methods

Self-healing radio maps of wireless networks for indoor positioning

Programa Doutoral em Telecomunicações MAP-tele das Universidades do Minho, Aveiro e PortoA Indústria 4.0 está a impulsionar a mudança para novas formas de produção e otimização em tempo real nos espaços industriais que beneficiam das capacidades da Internet of Things (IoT) nomeadamente, a localização de veículos para monitorização e optimização de processos. Normalmente os espaços industriais possuem uma infraestrutura Wi-Fi que pode ser usada para localizar pessoas, bens ou veículos, sendo uma oportunidade para aumentar a produtividade. Os mapas de rádio são importantes para os sistemas de posicionamento baseados em Wi-Fi, porque representam o ambiente de rádio e são usados para estimar uma posição. Os mapas de rádio são constituídos por amostras Wi-Fi recolhidas em posições conhecidas e degradam-se ao longo do tempo devido a vários fatores, por exemplo, efeitos de propagação, adição/remoção de APs, entre outros. O processo de construção do mapa de rádio costuma ser exigente em termos de tempo e recursos humanos, constituindo um desafio considerável. Os veículos, que operam em ambientes industriais podem ser explorados para auxiliar na construção de mapas de rádio, desde que seja possível localizá-los e rastreá-los. O objetivo principal desta tese é desenvolver um sistema de posicionamento para veículos industriais com mapas de rádio auto-regenerativos (capaz de manter os mapas de rádio atualizados). Os veículos são localizados através da fusão sensorial de Wi-Fi com sensores de movimento, que permitem anotar novas amostras Wi-Fi para o mapa de rádio auto-regenerativo. São propostas duas abordagens de fusão sensorial, baseadas em Loose Coupling e Tight Coupling, para a localização dos veículos. A abordagem Tight Coupling inclui uma métrica de confiança para determinar quando é que as amostras de Wi-Fi devem ser anotadas. Deste modo, esta solução não requer calibração nem esforço humano para a construção e manutenção do mapa de rádio. Os resultados obtidos em experiências sugerem que esta solução tem potencial para a IoT e a Indústria 4.0, especialmente em serviços de localização, mas também na monitorização, suporte à navegação autónoma, e interconectividade.Industry 4.0 is driving change for new forms of production and real-time optimization in factories, which benefit from the Industrial Internet of Things (IoT) capabilities to locate industrial vehicles for monitoring, improving safety, and operations. Most industrial environments have a Wi-Fi infrastructure that can be exploited to locate people, assets, or vehicles, providing an opportunity for enhancing productivity and interconnectivity. Radio maps are important for Wi-Fi-based Indoor Position Systems (IPSs) since they represent the radio environment and are used to estimate a position. Radio maps comprise a set of Wi- Fi samples collected at known positions, and degrade over time due to several aspects, e.g., propagation effects, addition/removal of Access Points (APs), among others, hence they should be periodically updated to maintain the IPS performance. The process to build and maintain radio maps is usually time-consuming and demanding in terms of human resources, thus being challenging to perform. Vehicles, commonly present in industrial environments, can be explored to help build and maintain radio maps, as long as it is possible to locate and track them. The main objective of this thesis is to develop an IPS for industrial vehicles with self-healing radio maps (capable of keeping radio maps up to date). Vehicles are tracked using sensor fusion of Wi-Fi with motion sensors, which allows to annotate new Wi-Fi samples to build the self-healing radio maps. Two sensor fusion approaches based on Loose Coupling and Tight Coupling are proposed to track vehicles. The Tight Coupling approach includes a reliability metric to determine when Wi-Fi samples should be annotated. As a result, this solution does not depend on any calibration or human effort to build and maintain the radio map. Results obtained in real-world experiments suggest that this solution has potential for IoT and Industry 4.0, especially in location services, but also in monitoring and analytics, supporting autonomous navigation, and interconnectivity between devices.MAP-Tele Doctoral Programme scientific committee and the FCT (Fundação para a Ciência e Tecnologia) for the PhD grant (PD/BD/137401/2018

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

A two phase framework for visible light-based positioning in an indoor environment: performance, latency, and illumination

Recently with the advancement of solid state lighting and the application thereof to Visible Light Communications (VLC), the concept of Visible Light Positioning (VLP) has been targeted as a very attractive indoor positioning system (IPS) due to its ubiquity, directionality, spatial reuse, and relatively high modulation bandwidth. IPSs, in general, have 4 major components (1) a modulation, (2) a multiple access scheme, (3) a channel measurement, and (4) a positioning algorithm. A number of VLP approaches have been proposed in the literature and primarily focus on a fixed combination of these elements and moreover evaluate the quality of the contribution often by accuracy or precision alone. In this dissertation, we provide a novel two-phase indoor positioning algorithmic framework that is able to increase robustness when subject to insufficient anchor luminaries and also incorporate any combination of the four major IPS components. The first phase provides robust and timely albeit less accurate positioning proximity estimates without requiring more than a single luminary anchor using time division access to On Off Keying (OOK) modulated signals while the second phase provides a more accurate, conventional, positioning estimate approach using a novel geometric constrained triangulation algorithm based on angle of arrival (AoA) measurements. However, this approach is still an application of a specific combination of IPS components. To achieve a broader impact, the framework is employed on a collection of IPS component combinations ranging from (1) pulsed modulations to multicarrier modulations, (2) time, frequency, and code division multiple access, (3) received signal strength (RSS), time of flight (ToF), and AoA, as well as (4) trilateration and triangulation positioning algorithms. Results illustrate full room positioning coverage ranging with median accuracies ranging from 3.09 cm to 12.07 cm at 50% duty cycle illumination levels. The framework further allows for duty cycle variation to include dimming modulations and results range from 3.62 cm to 13.15 cm at 20% duty cycle while 2.06 cm to 8.44 cm at a 78% duty cycle. Testbed results reinforce this frameworks applicability. Lastly, a novel latency constrained optimization algorithm can be overlaid on the two phase framework to decide when to simply use the coarse estimate or when to expend more computational resources on a potentially more accurate fine estimate. The creation of the two phase framework enables robust, illumination, latency sensitive positioning with the ability to be applied within a vast array of system deployment constraints

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications