19,675 research outputs found

    Interactive virtual indoor navigation system using visual recognition and pedestrian dead reckoning techniques

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    Finding a destination in an unfamiliar indoor environment requires cumbersome effort to refer to a physical floor plan or directory to locate the intended destination. With the advancements of mobile technologies, a navigational system using mobile computing devices such as mobile phone or tablet could aid users in locating the desired destination with ease. This paper presented an interactive virtual indoor navigation system which is developed for Sunway University campus. In order to provide an interactive and context-sensitive navigation platform, a hybrid solution has been proposed by blending the sensor capabilities on the mobile devices to work in an indoor environment. These sensors include utilizing the built-in accelerometer, compass and camera capabilities to create an interactive content of indoor navigation system using visual recognition and pedestrian dead reckoning for Augmented Reality (AR). Furthermore, user satisfaction and feedback survey have been collected for further improvement the proposed solution

    An indoor navigation architecture using variable data sources for blind and visually impaired persons

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    Contrary to outdoor positioning and navigation systems, there isn’t a counterpart global solution for indoor environments. Usually, the deployment of an indoor positioning system must be adapted case by case, according to the infrastructure and the objective of the localization. A particularly delicate case is related with persons who are blind or visually impaired. A robust and easy to use indoor navigation solution would be extremely useful, but this would also be particularly difficult to develop, given the special requirements of the system that would have to be more accurate and user friendly than a general solution. This paper presents a contribute to this subject, by proposing a hybrid indoor positioning system adaptable to the surrounding indoor structure, and dealing with different types of signals to increase accuracy. This would permit lower the deployment costs, since it could be done gradually, beginning with the likely existing Wi-Fi infrastructure to get a fairy accuracy up to a high accuracy using visual tags and NFC tags when necessary and possible.info:eu-repo/semantics/publishedVersio

    Exploring Hybrid Indoor Positioning Systems

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    Ubiquitous applications collect contextual information, process it, and then use this derived data to deliver valuable services. Location is one these contexts, and has been significant in providing navigation and guidance services for GPS devices. However, GPS is designed for outdoor use and is not precise enough, in terms of location accuracy for indoor applications. There are many indoor location systems that rely on a single technology, but these systems are either inaccurate in uncontrolled environments or require the installation of a dedicated infrastructure. This has led to the investigation of hybrid systems. This thesis examines the creation of a hybrid indoor positioning system combining different tech­ nologies and techniques; Wi-Fi access points and their associated signal strength, image analysis using machine learning to create location specific scene classifiers, and an altimeter sensor to determine the user\u27s current floor. This system is meant to provide indoor positioning data to location-aware applications

    A Survey of Positioning Systems Using Visible LED Lights

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    © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.As Global Positioning System (GPS) cannot provide satisfying performance in indoor environments, indoor positioning technology, which utilizes indoor wireless signals instead of GPS signals, has grown rapidly in recent years. Meanwhile, visible light communication (VLC) using light devices such as light emitting diodes (LEDs) has been deemed to be a promising candidate in the heterogeneous wireless networks that may collaborate with radio frequencies (RF) wireless networks. In particular, light-fidelity has a great potential for deployment in future indoor environments because of its high throughput and security advantages. This paper provides a comprehensive study of a novel positioning technology based on visible white LED lights, which has attracted much attention from both academia and industry. The essential characteristics and principles of this system are deeply discussed, and relevant positioning algorithms and designs are classified and elaborated. This paper undertakes a thorough investigation into current LED-based indoor positioning systems and compares their performance through many aspects, such as test environment, accuracy, and cost. It presents indoor hybrid positioning systems among VLC and other systems (e.g., inertial sensors and RF systems). We also review and classify outdoor VLC positioning applications for the first time. Finally, this paper surveys major advances as well as open issues, challenges, and future research directions in VLC positioning systems.Peer reviewe

    State Estimator using Hybrid Kalman and Particle Filter for Indoor UAV Navigation

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    Master's thesis in Mechatronics (MAS500)Unmanned aerial vehicles (UAVs) are being used for outdoors inspection and surveying tasks. When operating in an outdoor environment, the global navigation satellite system (GNSS) is predominantly used for position aiding, and magnetometers are used for heading aiding. In combination with an inertial sensor, these sensors form the backbone for state estimation for drones operating in an outdoor environment. A desire to utilize UAVs for inspections in indoor environments means that new challenges are faced. One of these challenges is that the traditional GNSS is unavailable for position aiding, and magnetometers can be unreliable in the presence of industrial equipment. This thesis aims at proposing, developing, and implementing a filtering solution capable of performing indoor autonomous navigation. A Hybrid filter solution is proposed where the GNSS and magnetometer are replaced by a stereo camera for depth perception. The Hybrid-filter is composed of a Kalman filter loosely coupled with a Particle filter. The Kalman filter is the main navigation filter in this framework. The navigation solution is based on integrated inertial measurements and aided by position and heading estimates from the Particle filter. In turn, the particle filter utilizes the velocity and attitude estimates from the Kalman filter, along with the depth data from the stereo camera to estimate the position and heading of the UAV

    Joint received signal strength, angle-of-arrival, and time-of-flight positioning

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    This paper presents a software positioning framework that is able to jointly use measured values of three parameters: the received signal strength, the angle-of-arrival, and the time-of-flight of the wireless signals. Based on experimentally determined measurement accuracies of these three parameters, results of a realistic simulation scenario are presented. It is shown that for the given configuration, angle-of-arrival and received signal strength measurements benefit from a hybrid system that combines both. Thanks to their higher accuracy, time-of-flight systems perform significantly better, and obtain less added value from a combination with the other two parameters

    Integrated WiFi/PDR/Smartphone using an unscented Kalman filter algorithm for 3D indoor localization

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    Because of the high calculation cost and poor performance of a traditional planar map when dealing with complicated indoor geographic information, a WiFi fingerprint indoor positioning system cannot be widely employed on a smartphone platform. By making full use of the hardware sensors embedded in the smartphone, this study proposes an integrated approach to a three-dimensional (3D) indoor positioning system. First, an improved K-means clustering method is adopted to reduce the fingerprint database retrieval time and enhance positioning efficiency. Next, with the mobile phone’s acceleration sensor, a new step counting method based on auto-correlation analysis is proposed to achieve cell phone inertial navigation positioning. Furthermore, the integration of WiFi positioning with Pedestrian Dead Reckoning (PDR) obtains higher positional accuracy with the help of the Unscented Kalman Filter algorithm. Finally, a hybrid 3D positioning system based on Unity 3D, which can carry out real-time positioning for targets in 3D scenes, is designed for the fluent operation of mobile terminals

    Hybrid Building/Floor Classification and Location Coordinates Regression Using A Single-Input and Multi-Output Deep Neural Network for Large-Scale Indoor Localization Based on Wi-Fi Fingerprinting

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    In this paper, we propose hybrid building/floor classification and floor-level two-dimensional location coordinates regression using a single-input and multi-output (SIMO) deep neural network (DNN) for large-scale indoor localization based on Wi-Fi fingerprinting. The proposed scheme exploits the different nature of the estimation of building/floor and floor-level location coordinates and uses a different estimation framework for each task with a dedicated output and hidden layers enabled by SIMO DNN architecture. We carry out preliminary evaluation of the performance of the hybrid floor classification and floor-level two-dimensional location coordinates regression using new Wi-Fi crowdsourced fingerprinting datasets provided by Tampere University of Technology (TUT), Finland, covering a single building with five floors. Experimental results demonstrate that the proposed SIMO-DNN-based hybrid classification/regression scheme outperforms existing schemes in terms of both floor detection rate and mean positioning errors.Comment: 6 pages, 4 figures, 3rd International Workshop on GPU Computing and AI (GCA'18
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