18 lines
1.3 KiB
TeX
18 lines
1.3 KiB
TeX
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\begin{abstract}
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DUMMY ABSTRACT. We present an indoor localisation system that integrates different sensor modalities,
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namely Wi-Fi, barometer, iBeacons, step- and turn-detection for localisation of pedestrians within buildings
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over multiple floors. To model the pedestrian's movement, which is constrained by walls and other obstacles,
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we propose a state transition based upon random walks on graphs. \sout{This model also frees us from the burden of
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frequently updating the system.}
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In addition we make use of barometer information to estimate the current floor.
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\commentByFrank{entweder alle sensoren nennen, oder weglassen? sonst wirkt es nicht schluessig}ds
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Furthermore, we present a statistical approach to avoid the incorporation of faulty heading information caused
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by changing the smartphone's position.
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\commentByFrank{ueber statistical reden wir nochma. einerseits ja, andererseits irgendwie nein.}
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The evaluation of the system within a $\SI{77}{\meter}$ $\times$ $\SI{55}{\meter}$ sized building with 4 floors
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shows that high accuracy can be achieved while also keeping the update-rates low.
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\commentByFrank{We will show that incorporating prior knowledge, such as the pedestrian's desired destination,
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improves the overall localisation process and prevents various error-conditions.}
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\end{abstract}
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