\section{Conclusion}

%what you have seen
Within this work we provided an extensive overview of our smartphone-based indoor localization system, \add{providing both, previous advances and novel contributions.}
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. 
\add{Based on the good results in this challenging scenario, we believe that our solution can be adapted to many other public buildings and environments, resulting in a very generally usable  solution for self-localization of pedestrians using smartphones.
Previous versions of the system have already proven themselves in other, more modern buildings, which supports this claim to general use.}

%novel stuff
\add{Thanks to the novel contributions presented, we have been able to further increase the robustness and accuracy of the system.
To a large extent, this was achieved by using the navigation mesh. 
It allows to map continuous movements and enables to reduce the map sizes to only a few megabytes for a complete building. 
The problem of sample impoverishment can be addressed easily by incorporating the here presented method onto the state transition of the particle filter.  
In combination with the threshold-based activity recognition, both methods further enhance the robustness.}
Given the improvements above and those achieved in previous works, 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. 
\add{The system should require as little manual effort as possible. 
This is mainly achieved by the optimization scheme, providing all necessary parameters for the Wi-Fi model.}
The localization runs solely an a commercial smartphone, thus no connection to a server or the Wi-Fi infrastructure is required.


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 \del{rapid computation} \add{approximation} 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.

