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toni
2016-05-09 16:52:20 +02:00
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tex/chapters/experiments.tex
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@@ -98,14 +98,14 @@ The BS has a similar improvement rate.
\begin{figure}
\centering
\input{gfx/eval/interval_path2_compare/path2_interval_compare}
\caption{Left: Exemplary results for path 2 where BS (blue) and filtering (green) using 2500 particles and 500 sample realisations. Right: A situation where smoothing provides a worse error in regard to the ground truth, but obviously a more realistic path.}
\caption{a) Exemplary results for path 2 where BS (blue) and filtering (green) using 2500 particles and 500 sample realisations. b) A situation where smoothing provides a worse error in regard to the ground truth, but obviously a more realistic path.}
\label{fig:int_path2}
\end{figure}
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Two visual examples of the smoothing outcome for path 2 are illustrated in fig. \ref{fig:int_path2}.
It can be clearly seen, how the smoothing compensates for the faulty detected floor changes using future knowledge.
Additionally, the initial error is reduced extremely, approximating the pedestrian's starting position down to a few centimetres.
In the context of reducing the error as far as possible, the right side of fig. \ref{fig:int_path2} is a very interesting example.
In the context of reducing the error as far as possible, fig. \ref{fig:int_path2} b) is a very interesting example.
Here, the filter offers a lower approximation and positional error in regard to the ground truth.
However it is obvious that smoothing causes the estimation to behave more natural instead of walking the supposed path.
This phenomena could be observed for both smoothers respectively.