FHWS/IPIN2018
Archived
4
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

transition eval

Browse Source
This commit is contained in:
toni
2018-09-14 11:31:19 +02:00
parent 85491a0330
commit ade1677fa4
1 changed files with 39 additions and 12 deletions
Download Patch File Download Diff File Expand all files Collapse all files

51
tex/chapters/experiments.tex
View File

@@ -33,43 +33,70 @@ Finally, the respective estimation methods are discussed in section \ref{sec:eva
\def\svgwidth{\columnwidth}
\input{gfx/transEval/mesh_25_final.eps_tex}
\caption{Mesh after 25 steps}
\label{fig:transitionEval:a}
\end{subfigure}
\hspace{2cm}
\begin{subfigure}{0.4\textwidth}
\def\svgwidth{\columnwidth}
\input{gfx/transEval/grid_25_final.eps_tex}
\caption{Graph after 25 steps}
\label{fig:transitionEval:b}
\end{subfigure}
\begin{subfigure}{0.4\textwidth}
\def\svgwidth{\columnwidth}
\input{gfx/transEval/mesh_180_final.eps_tex}
\caption{Mesh after 180 steps}
\label{fig:transitionEval:c}
\end{subfigure}
\hspace{2cm}
\begin{subfigure}{0.4\textwidth}
\def\svgwidth{\columnwidth}
\input{gfx/transEval/grid_180_final.eps_tex}
\caption{Graph after 180 steps}
\label{fig:transitionEval:d}
\end{subfigure}
\caption{Simple staircase scenario to compare the graph-based model with the navigation mesh. The black line indicates the current position and the green line gives the estimated path until 25 or 180 steps, both using weighted average. The particles are coloured according to their height. A pedestrian walks up and down the stairs several times in a row. After 25 steps, both methods produce good results, although there are already some outliers (blue particles). After 180 steps, the outliers using the graph have multiplied, leading to a multimodal situation. In contrast, the mesh offers the possibility to remove particles that hit a wall and can thus prevent such a situation.}
\label{fig:transitionEval}
\end{figure}
To compare our old graph-based model with our novel transition model presented within section \ref {sec:transition}, we chose a simple scenario, in which a tester walks up and down a staircase several times.
We used 1000 particles and did not perform an evaluation and resampling step to maintain the pure performance of the transition (step and heading).
The filter starts at a fixed position and is updated after every newly recognized step.
We set $\sigma_\text{step} = 0.1$ and $\sigma_\text{turn} = 0.1$ likewise.
The cells of the gridded graph were \SI{20}{} x \SI{20}{\centi\meter} in size and the transition implemented as described in \cite{Ebner-16}.
As described in section \ref {sec:transition}, the mesh demands for a strategy, how to handle unreachable destinations.
We chose a simple, yet effective strategy: whenever a destination is unreachable to a particle, it is removed and the last correct transitioning particle is duplicated.
Of course, the graph does not require for such a rule, since particles are only allowed to move on nodes and search for neighbours.
To make a statement about the performance of our novel transition model presented within section \ref {},
we chose a simple scenario, in which a tester walks up and down a staircase three times.
Fig. \ref{fig:transitionEval:a} and \ref{fig:transitionEval:b} illustrate the results after \SI{25}{steps} for each method.
The particles are coloured according to their height and the walking path (green line) is estimated using weighted-average.
It can be seen that both methods provide similar results.
Due to the discrete grid structure, the purple particles on the graph scatter more strongly, while the mesh provides a truly continuous structure and thus a more compact representation.
It is important to note that outliers have already appeared in both scenarios (blue particles).
Due to the included sensor noise, they covered a too short distance for several times and thus the upcoming left turn leads upwards instead of downwards.
Going straightforward to \SI{180} steps, this phenomenon has multiplied for the graph (cf. fig. \ref{fig:transitionEval:d}), but not for the mesh (cf. fig. \ref{fig:transitionEval:c}).
This is due to the above-mentioned strategy for the mesh.
Compared to this approach, the graph is not able to remove any particles and thus they walk according to the recognized steps and heading changes, even if they theoretically hit a wall several times.
The resulting effects are obvious.
After walking up and down twice, several particle groups have formed, which no longer allows an accurate position estimation.
fig shows a situation in which the new transition model is able to perform better due to choosing a clever tactic in this situation.
particles hitting a wall are remove, while on grid we do not know if a wall is hit, since particles can only move on nodes and search for neaboring nodes..
at 25 can see that some particles walking nach oben both scenarios..
at 180 in grid das fehlverhalten kummuliert während bei der coolen taktik der neuen transition nicht.
of course, can be fixed adding an activiy if up or down or incorporating a barometer.
aber es ist eine gute sache wenn ein sensor für sich schon gute ergebnisse liefert.
er wird dadurch unabhängiger und kann beim ausfall der anderen sensoren tdz. gute ergebnisse liefern.
weiterhin, neue transition more continues etc pp.
der speicherbedarf von --- nach ---, voll toll und praktisch.
nachteil, erhöhte rechenzeit bei neuer trans.
Of course, a similar strategy could be developed for a graph.
We have already shown how to identify the nodes nearest to walls in one of our previous works \cite{Ebner-16}.
However, the limitation to walk in \SI{45}{\degree} angles as well as the discrete cell sizes lead to restrictions for small rooms, narrow hallways or bigger cells.
For example walking through a door, would result in a strong reduction of differing particles.
If the state evaluation is then used to assigned weights to particles, the crucial problem of sample degeneracy often occurs.
With a mesh, on the other hand, walkable destinations can also be located in a room behind a wall.
In combination with the continues movement, this allows for a high versatility of particles even in such situations.
Another method to fix the problems shown in fig. \ref{fig:transitionEval:d}, is by adding an activity recognition (walking up, down straight) or to incorporate a barometer.
Nevertheless, in most cases it is an advantage if a sensor model delivers good results on its own, without further dependencies.
For example, if a sensor is currently unavailable or damaged, the system is still able to provide prober results.
Besides the advantages the mesh offers, it also has a few disadvantages compared to the graph.
The computation time has increased due to the calculation of reachable destinations.
With the graph, only the direct neighbours are of interest, which can be implemented very efficiently using a tree structure.
Further, the graph allows the easily store meta information on its nodes, for example Wi-Fi fingerprints or precalculations for shortest-path methods.
This is more difficult using the mash and requires the handling of baricentric coordinates.
\subsection{\docWIFI{} Optimization}