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toni
2018-07-23 15:48:18 +02:00
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tex/chapters/abstract.tex
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@@ -12,7 +12,7 @@ The goal of this work is to propose a fast to deploy and low-cost localization s
provides reasonable results in a high variety of situations.
Therefore, we have chosen a very challenging test scenario.
All experiments were conducted within a 13th century historic building, formerly a convent and today a museum.
The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{600}{\meter} length and \SI{10}{\minute} duration.
The system is evaluated using 28 distinct measurement series on four different test walks, up to \SI{310}{\meter} length and \SI{10}{\minute} duration.
It can be shown, that the here presented localization solution is able to provide a small positioning error, even under difficult conditions and faulty measurements.
Our advanced filtering methods allow for a real fail-safe system, while the novel optimization scheme enables a setup-time of under \SI{120}{\minute} for the complete building.
%We are able to resolve sample impoverishment whenever it occurs and thus provide a real fail-safe system.

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tex/chapters/conclusion.tex
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\section{Conclusion}
KDE besser machen, nicht nur maximumg
%what you have seen
Within this work we provided an extensive overview of our smartphone-based indoor localization system.
The thorough evaluation demonstrated the good performance under multiple scenarios within a complex environment.
The system is able to handle problems like sample impoverishment and multimodal densities, occurring through the us of a particle filtering scheme.
The main advantage of our approach is its suitability for practical use.
Compared to other state-of-the-art solutions, the setup time is only a few hours and does not require any expert knowledge or hardware.
The localization runs solely an a commercial smartphone, thus no connection to a server or the Wi-Fi infrastructure is required.
By using navigation meshes we are able to reduce the map sizes to only a few megabytes for a complete building.
simple impoverishment nur dann einschalten wenn wirklich gebraucht
Nevertheless, there is still room for further improvements and future work.
Through the change from a graph to a mesh, we lost the ability to easily find the shortest path for navigation purposes as described in \cite{Ebner-16}.
By means of barycentric coordinates, this should however be easily adaptable to the triangular structure.
The threshold-based activity recognition is not able to distinguished between different types of elevation, namely elevator, escalator and stairs.
Especially in buildings where elevators pass many floors, the transition fails to move particles in the according speed.
Here, we need to incorporate special environmental knowledge about elevators and escalators or again integrate a probabilistic sensor model for the barometer as already done in previous works \cite{Ebner-15}.
wifi optimierung noch besser machen und wände mitnehmen also raytracing
A crucial point to further increase the accuracy of the system is the choice of the signal strength prediction model.
At the moment we consider only the attenuation per floor, however by including information about walls and other obstacles, we should be able to decrease the error at the cost of additional computations.
Instead of providing those additional environmental informations by manual measurements, the optimization scheme could be used to approximate the respective model and material parameters.
Special data-structures for pre-computation combined with online interpolation might then be a viable choice for utmost accuracy that is still able to run on a commercial smartphone in real-time.
Finally, the rapid computation scheme for the KDE opens up completely new possibilities when handling particle sets.
Within this paper we used it to find the real global maxima for a state estimation and to accurately calculate the Kullback-Leibler divergence.
However, many other estimation schemes are thinkable, for example a trajectory based one, with multiple path-hypotheses, each weighted based on a-priori knowledge.
The KDE approach could also be used to develop better suited resampling techniques, by enabling to draw particles from the underlying density, instead of just reproducing known owns.
Conclusion Conclusion