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toni
2016-05-11 16:30:36 +02:00
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tex/chapters/introduction.tex
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@@ -69,7 +69,7 @@ Further critical problems arise from multimodal distributions.
Those are caused by multiple possible position estimates.
Fig. \ref{fig:multimodalPath} illustrates an example where a floor gets separated by a wall.
Due to inaccurate measurements and a PDR approach for evaluating the movement, the distribution splits apart.
Therefore, the weighted average position is somewhere in-between.
Depending on the chosen estimator, the approximated position might be located somewhere in-between as seen in fig. \ref{fig:multimodalPath}.
Only after the pedestrian turns right, the distribution is again unimodal, since moving through walls is impossible.
As one can imagine, this can lead to serious problems in big indoor environments.
Such a situation can be improved by incorporating future measurements (e.g. the right turn)
@@ -77,7 +77,7 @@ Such a situation can be improved by incorporating future measurements (e.g. the
to the filtering procedure \cite{Ebner-16}.
However, standard filtering methods are not able to use any future information and the possibilities to make a distant forecast are also limited \cite{robotics, Doucet11:ATO, chen2003bayesian}.
One very promising way to deal with these problems is smoothing.
One promising way to deal with these problems is smoothing.
Smoothing methods are able to make use of future measurements for computing their estimation.
By running backwards in time, they are also able to remove multimodalities and improve the overall localisation result.
Since the problem of navigation, especially the representation of complex movement patterns, results in a non-linear and non-Gaussian state space, this work focuses mainly on smoothing techniques based on the broad class of MC methods.
@@ -87,7 +87,7 @@ Namely, forward-backward smoothing \cite{doucet2000} and backward simulation \ci
Within this work, we investigate the benefits and drawbacks of those techniques using a conventional localisation system \cite{Ebner-16}.
We provide both, fixed-lag and fixed-interval smoothing as well as a novel approach for incorporating them easily within the localisation procedure.
Additionally, we enrich the state transition model with an activity recognition to distinguish between walking, standing and walking stairs.
The main goal is to solve above mentioned problems and to investigate new possibilities for even more advanced systems.
The main goal is to solve the above mentioned problems and to investigate new possibilities for even more advanced systems.
All of our contributions are supported by an extensive experimental evaluation.