\section{Conclusion}

%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 use 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. 

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}. 

A crucial point to further increase the accuracy of the system is the choice of the signal strength prediction model. 
Currently 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.

