112 research outputs found

    Recent Advances in Indoor Localization Systems and Technologies

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    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

    Indoor navigation for the visually impaired : enhancements through utilisation of the Internet of Things and deep learning

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    Wayfinding and navigation are essential aspects of independent living that heavily rely on the sense of vision. Walking in a complex building requires knowing exact location to find a suitable path to the desired destination, avoiding obstacles and monitoring orientation and movement along the route. People who do not have access to sight-dependent information, such as that provided by signage, maps and environmental cues, can encounter challenges in achieving these tasks independently. They can rely on assistance from others or maintain their independence by using assistive technologies and the resources provided by smart environments. Several solutions have adapted technological innovations to combat navigation in an indoor environment over the last few years. However, there remains a significant lack of a complete solution to aid the navigation requirements of visually impaired (VI) people. The use of a single technology cannot provide a solution to fulfil all the navigation difficulties faced. A hybrid solution using Internet of Things (IoT) devices and deep learning techniques to discern the patterns of an indoor environment may help VI people gain confidence to travel independently. This thesis aims to improve the independence and enhance the journey of VI people in an indoor setting with the proposed framework, using a smartphone. The thesis proposes a novel framework, Indoor-Nav, to provide a VI-friendly path to avoid obstacles and predict the user s position. The components include Ortho-PATH, Blue Dot for VI People (BVIP), and a deep learning-based indoor positioning model. The work establishes a novel collision-free pathfinding algorithm, Orth-PATH, to generate a VI-friendly path via sensing a grid-based indoor space. Further, to ensure correct movement, with the use of beacons and a smartphone, BVIP monitors the movements and relative position of the moving user. In dark areas without external devices, the research tests the feasibility of using sensory information from a smartphone with a pre-trained regression-based deep learning model to predict the user s absolute position. The work accomplishes a diverse range of simulations and experiments to confirm the performance and effectiveness of the proposed framework and its components. The results show that Indoor-Nav is the first type of pathfinding algorithm to provide a novel path to reflect the needs of VI people. The approach designs a path alongside walls, avoiding obstacles, and this research benchmarks the approach with other popular pathfinding algorithms. Further, this research develops a smartphone-based application to test the trajectories of a moving user in an indoor environment

    Laitteiden välisen yhteistyön soveltuvuus älypuhelimilla toteutettavaan sisätilapaikannukseen

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    A reliable indoor positioning service for smartphones is a service that is often requested. There are several competing technologies already available but a lot of basic research is still done on the subject. This thesis studies the applicability and technological possibilities of improving the performance of a positioning service using peer to peer collaboration. The Bluetooth low energy technology (BLE) offers a possibility to use peer to peer radio signal measurements with smartphones. This could be used to improve the performance of existing positioning algorithms if enough service users are in close proximity to each other. In this thesis a pedestrian simulation system was implemented to study the probability that two positioning service users are in close enough proximity to each other for BLE usage. The suitability of BLE as the collaboration technology was studied by implementing a particle filter based positioning system that uses BLE measurements to track a smartphone. Finally the collaborative BLE system was integrated on top of an existing geomagnetic tracking algorithm and the effect on the positioning performance was studied. It was concluded that the BLE as a technology is suitable for positioning use despite the large measurement uncertainty. BLE based collaboration is feasible in improving the positioning results provided that the basic positioning technology is reliable enough. The pedestrian simulations concluded that with realistic expected number of users in one building most sessions would not benefit from collaboration but it would still likely happen frequently.Luotettava sisätilapaikannuspalvelu on haluttu ominaisuus mobiilipalveluiden kehityksessä. Useita kilpailevia ratkaisuja on jo markkinoilla, mutta ongelman parissa tehdään vielä huomattavan paljon perustutkimusta. Tässä diplomityössä tutkitaan mahdollisuutta parantaa paikannusjärjestelmän toimintaa käyttäen vertaisyhteistyötä. Bluetooth low energy -teknologia (BLE) tarjoaa mahdollisuuden käyttää laitteiden välisiä radiosignaalimittauksia älypuhelimilla. Tätä voidaan mahdollisesti hyödyntää parantamaan olemassa olevien paikannusalgoritmien toimintaa, jos riittävästi käyttäjiä on riittävän lähellä toisiaan. Tässä diplomityössä toteutettiin ihmisjoukkojen liikettä sisätiloissa mallintava järjestelmä, jolla tutkittiin todennäköisyyttä, että kaksi paikannusjärjestelmän käyttäjää olisi riittävän lähellä toisiaan käyttääkseen BLE-radiomittauksia. BLE:n soveltuvuutta paikannusteknologiana tutkittiin toteuttamalla partikkelisuotimeen perustuva paikannusjärjestelmä, joka käyttää BLE-mittauksia älypuhelimen seuraamiseen. Lopuksi BLE mittausjärjestelmä integroitiin olemassa olevaan magneettikenttään perustuvaan paikannusalgoritmiin ja BLE-yhteistyön vaikutusta algoritmin toimintaan tutkittiin. Työ osoitti, että BLE on paikannuskäyttöön soveltuva teknologia suuresta mittausepävarmuudesta huolimatta. BLE-perusteinen yhteistyö paikannustuloksen parantamisessa on toimiva ratkaisu, mikäli varsinainen paikannusteknologia on riittävän luotettava. Realistisesti odotettavissa olevilla paikannuspalvelun käyttäjämäärillä BLE-yhteistyötä todennäköisesti tapahtuisi suhteellisen usein, vaikka suurin osa paikannussessioista ei pääsisikään hyötymään siitä

    Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

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    The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

    Deep Learning Methods for Fingerprint-Based Indoor and Outdoor Positioning

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    Outdoor positioning systems based on the Global Navigation Satellite System have several shortcomings that have deemed their use for indoor positioning impractical. Location fingerprinting, which utilizes machine learning, has emerged as a viable method and solution for indoor positioning due to its simple concept and accurate performance. In the past, shallow learning algorithms were traditionally used in location fingerprinting. Recently, the research community started utilizing deep learning methods for fingerprinting after witnessing the great success and superiority these methods have over traditional/shallow machine learning algorithms. The contribution of this dissertation is fourfold: First, a Convolutional Neural Network (CNN)-based method for localizing a smartwatch indoors using geomagnetic field measurements is presented. The proposed method was tested on real world data in an indoor environment composed of three corridors of different lengths and three rooms of different sizes. Experimental results show a promising location classification accuracy of 97.77% with a mean localization error of 0.14 meter (m). Second, a method that makes use of cellular signals emitting from a serving eNodeB to provide symbolic indoor positioning is presented. The proposed method utilizes Denoising Autoencoders (DAEs) to mitigate the effects of cellular signal loss. The proposed method was evaluated using real-world data collected from two different smartphones inside a representative apartment of eight symbolic spaces. Experimental results verify that the proposed method outperforms conventional symbolic indoor positioning techniques in various performance metrics. Third, an investigation is conducted to determine whether Variational Autoencoders (VAEs) and Conditional Variational Autoencoders (CVAEs) are able to learn the distribution of the minority symbolic spaces, for a highly imbalanced fingerprinting dataset, so as to generate synthetic fingerprints that promote enhancements in a classifier\u27s performance. Experimental results show that this is indeed the case. By using various performance evaluation metrics, the achieved results are compared to those obtained by two state-of-the-art oversampling methods known as Synthetic Minority Oversampling TEchnique (SMOTE) and ADAptive SYNthetic (ADASYN) sampling. Fourth, a novel dataset of outdoor location fingerprints is presented. The proposed dataset, named OutFin, addresses the lack of publicly available datasets that researchers can use to develop, evaluate, and compare fingerprint-based positioning solutions which can constitute a high entry barrier for studies. OutFin is comprised of diverse data types such as WiFi, Bluetooth, and cellular signal strengths, in addition to measurements from various sensors including the magnetometer, accelerometer, gyroscope, barometer, and ambient light sensor. The collection area spanned four dispersed sites with a total of 122 Reference Points (RPs). Before OutFin was made available to the public, several experiments were conducted to validate its technical quality

    Achieving Practical and Accurate Indoor Navigation for People with Visual Impairments

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    Methods that provide accurate navigation assistance to people with visual impairments often rely on instrumenting the environment with specialized hardware infrastructure. In particular, approaches that use sensor networks of Bluetooth Low Energy (BLE) beacons have been shown to achieve precise localization and accurate guidance while the structural modifications to the environment are kept at minimum. To install navigation infrastructure, however, a number of complex and time-critical activities must be performed. The BLE beacons need to be positioned correctly and samples of Bluetooth signal need to be collected across the whole environment. These tasks are performed by trained personnel and entail costs proportional to the size of the environment that needs to be instrumented. To reduce the instrumentation costs while maintaining a high accuracy, we improve over a traditional regression-based localization approach by introducing a novel, graph-based localization method using Pedestrian Dead Reckoning (PDR) and particle filter. We then study how the number and density of beacons and Bluetooth samples impact the balance between localization accuracy and set-up cost of the navigation environment. Studies with users show the impact that the increased accuracy has on the usability of our navigation application for the visually impaired
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