Indoor Localization Solutions for a Marine Industry Augmented Reality Tool

In this report are described means for indoor localization in special, challenging circum-stances in marine industry. The work has been carried out in MARIN project, where a tool based on mobile augmented reality technologies for marine industry is developed. The tool can be used for various inspection and documentation tasks and it is aimed for improving the efficiency in design and construction work by offering the possibility to visualize the newest 3D-CAD model in real environment. Indoor localization is needed to support the system in initialization of the accurate camera pose calculation and auto-matically finding the right location in the 3D-CAD model. The suitability of each indoor localization method to the specific environment and circumstances is evaluated.Siirretty Doriast

Impact of Positioning Technology on Human Navigation

In navigation from one place to another, spatial knowledge helps us establish a destination and route while travelling. Therefore, sufficient spatial knowledge is a vital element in successful navigation. To build adequate spatial knowledge, various forms of spatial tools have been introduced to deliver spatial information without direct experience (maps, descriptions, pictures, etc.). An innovation developed in the 1970s and available on many handheld platforms from the early 2000s is the Global Position System (GPS) and related map and text-based navigation support systems. Contemporary technical achievements, such as GPS, have made navigation more effective, efficient, and comfortable in most outdoor environments. Because GPS delivers such accurate information, human navigation can be supported without specific spatial knowledge. Unfortunately, there is no universal and accurate navigation system for indoor environments. Since smartphones have become increasingly popular, we can more frequently and easily access various positioning services that appear to work both indoors and outdoors. The expansion of positioning services and related navigation technology have changed the nature of navigation. For example, routes to destination are progressively determined by a “system,” not the individual. Unfortunately we only have a partial and nascent notion of how such an intervention affects spatial behaviour. The practical purpose of this research is to develop a trustworthy positioning system that functions in indoor environments and identify those aspects those should be considered before deploying Indoor Positioning System (IPS), all towards the goal of maintaining affordable positioning accuracy, quality, and consistency. In the same way that GPS provides worry free directions and navigation support, an IPS would extend such opportunities to many of our built environments. Unfortunately, just as we know little about how GPS, or any real time navigation system, affects human navigation, there is little evidence suggesting how such a system (indoors or outdoors) changes how we find our way. For this reason, in addition to specifying an indoor position system, this research examines the difference in human’s spatial behaviour based on the availability of a navigation system and evaluates the impact of varying the levels of availability of such tools (not available, partially available, or full availability). This research relies on outdoor GPS, but when such systems are available indoors and meet the accuracy and reliability or GPS, the results will be generalizable to such situations

Location-Enabled IoT (LE-IoT): A Survey of Positioning Techniques, Error Sources, and Mitigation

The Internet of Things (IoT) has started to empower the future of many industrial and mass-market applications. Localization techniques are becoming key to add location context to IoT data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) technologies have advantages such as long-range, low power consumption, low cost, massive connections, and the capability for communication in both indoor and outdoor areas. These features make LPWAN signals strong candidates for mass-market localization applications. However, there are various error sources that have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -- localization data sources -- localization algorithms -- localization error sources and mitigation -- localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-the-art review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors

Data Analysis and Memory Methods for RSS Bluetooth Low Energy Indoor Positioning

The thesis aims at finding a feasible solution to Bluetooth low energy indoor positioning (BLE-IP) including comprehensive data analysis of the received signal strength indication (RSSI) values. The data analysis of RSSI values was done to understand different factors influencing the RSSI values so as to gain better understanding of data generating process and to improve the data model. The positioning task is accomplished using a methodology called \textit{fingerprinting}. The fingerprinting based positioning involves two phases namely \textit{calibration phase} and \textit{localization phase}. The localization phase utilises the memory methods for positioning. In this thesis, we have used \textit{Gaussian process} for generation of radio maps and for localization we focus on memory methods: \textit{particle filters} and \textit{unscented Kalman filters}. The Gaussian process radio map is used as the measurement model in the Bayesian filtering context. The optimal fingerprinting phase parameters were determined and the filtering methods were evaluated in terms root mean square error

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

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