Personal Navigation System for Indoor Applications
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Navigation is usually associated with marine and aviation domains. Except for some lighthouses for orientation or landmarks near the coast, navigation instruments are essential because routes are virtual. Therefore, current position of ships and aircrafts must be drawn on the map. Land navigation is generally bound to the road infrastructure. Car navigation systems have been introduced to replace the use of road maps in order to assist the driver in the choise of the correct way...Keywords:
Navigational aid
Air navigation
Turn-by-turn navigation
Navigation System
Position (finance)
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In this paper, we present the development and the implementation of algorithms to access map databases by a user equipped with a pedestrian navigation system.
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In this paper an Indoor Navigation System with map-matching capabilities in real-time on a smart phone is presented. The basis of the system is an in-house development of an Integrated Pedestrian Navigation System, based on 2 low-cost IMUs, an electronic compass and an altimeter with a drifting navigation solution. Combining this system with an additional laser ranger and SLAM algorithms, we are able to build accurate maps of office buildings for already visited rooms in post processing. This paper presents a map matching algorithm based on a new reduced particle filter in order to use these maps later for real-time applications without an expensive laser ranger but relying only on the dual inertial system. It can be used with both, preprocessed SLAM maps or with already available maps. Finally to smooth the resulting trajectory after particle filtering we propose the use of a new "balanced bubble band smoother" allowing the trajectory to optimally match to both, map and recorded IMU data. This new approach makes it possible to do map matching online on a smart phone.
Map matching
Compass
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We introduce a novel self-contained seamless positioning solution for indoor and outdoor environments, well suited for and designed to be operated on off-the-shelf mobile phones. Position information is deduced from a combination of GNSS where available, combined with Pedestrian Dead Reckoning (PDR) utilizing inertial measurements and context-aware activity based map matching. The proposed system heavily exploits different types of human movement, such as walking, running, ascending or descending stairs, to improve the employed positioning model. In remaining independent from any external infrastructure, accurate localization is also possible in environments, where the installation and maintenance of such infrastructure does not make sense or is simply not affordable - as for example in a parking garage to guide a user to the next exit or back to his car. Concerning this particular use case we have also implemented an interface for the synchronization of location information between the mobile positioning solution and the car.
Stairs
Dead reckoning
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While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, we ask the question: can we perform indoor localization with no pre-deployment effort? Our setting is an indoor space, such as an office building or a mall, with WiFi coverage but where we do not assume knowledge of the physical layout, including the placement of the APs. Users carrying WiFi-enabled devices such as smartphones traverse this space in normal course. The mobile devices record Received Signal Strength (RSS) measurements corresponding to APs in their view at various (unknown) locations and report these to a localization server. Occasionally, a mobile device will also obtain and report a location fix, say by obtaining a GPS lock at the entrance or near a window. The centerpiece of our work is the EZ Localization algorithm, which runs on the localization server. The key intuition is that all of the observations reported to the server, even the many from unknown locations, are constrained by the physics of wireless propagation. EZ models these constraints and then uses a genetic algorithm to solve them. The results from our deployment in two different buildings are promising. Despite the absence of any explicit pre-deployment calibration, EZ yields a median localization error of 2m and 7m, respectively, in a small building and a large building, which is only somewhat worse than the 0.7m and 4m yielded by the best-performing but calibration-intensive Horus scheme [29] from prior work.
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The context sensitive indoor navigation system (CoINS) implements an architecture to develop context-aware indoor user guidance services and applications. This paper presents a detailed discussion on algorithms and architectural issues in building an indoor guidance system. We first start with the world model and required mapping to 2D for the process of path calculation and simplification. We also compare several algorithm optimizations applied in this particular context. The system provides the infrastructure to support different techniques of presenting the path and supporting user orientation to reach a certain destination in indoor premises
Context model
Context awareness
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Visibility
Visibility graph
3D city models
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Geometric networks
Data model (GIS)
Downtown
Spatial network
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In this paper, we present the development and the implementation of algorithms to access map databases by a user equipped with a pedestrian navigation system.
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The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
Location-based service
Fire-control radar
Man-portable radar
Tracking (education)
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