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@@ -38,9 +38,12 @@
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As uncertainties we used $\sigma_\text{wifi} = \sigma_\text{ib} = 8.0$, both growing with each measurement's age.
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While the pressure change was assumed to be \SI{0.105}{$\frac{\text{\hpa}}{\text{\meter}}$}, all other barometer-parameters
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are determined automatically (see \ref{sec:sensBaro}). The step size for the transition was configured to be \SI{70}{\centimeter}
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with an allowed derivation of \SI{10}{\percent}. The heading deviation in
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are determined automatically (see \ref{sec:sensBaro}). The step size $\mStepSize$ for the transition was configured to be
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\SI{70}{\centimeter} with an allowed derivation of \SI{10}{\percent}. The heading deviation in
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\refeq{eq:transSimple}, \refeq{eq:transShortestPath} and \refeq{eq:transMultiPath} was \SI{25}{\degree}.
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Edges departing from the pedestrian's destination are downvoted using $\mUsePath = 0.9$.
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\commentByFrank{$\mUsePath$ erklaert}
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As we start with a discrete uniform distribution for $\mStateVec_0$ (random position and heading), the first few estimations
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are omitted from the error calculation to allow the system to somewhat settle its initial state. Even though, the error
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@@ -71,9 +74,11 @@
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\begin{figure}
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\input{gfx/eval/error_timed_nexus}
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\caption{Development of the error while walking along Path 4 using the Motorola Nexus 6.
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When leaving the suggested route (3), the error of shortest path \refeq{eq:transShortestPath}
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and multipath \refeq{eq:transMultiPath} increases.
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The same issues arise when facing multimodalities between two staircases just before the destination (9).}
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When leaving the suggested route (3), the error of \textbf{shortest} path \refeq{eq:transShortestPath}
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and \textbf{multi}path \refeq{eq:transMultiPath} increases.
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The same issues arise when facing multimodalities between two staircases just before the destination (9).
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\commentByFrank{hilft das bold vlt. schon um die legende zu verstehen?}
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}
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\label{fig:errorTimedNexus}
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\end{figure}
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% detailed analysis of path 4
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@@ -133,8 +138,9 @@
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% overall error-distribution for nexus and galaxy
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\begin{figure}
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\input{gfx/eval/error_dist_nexus}
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\caption{Error distribution for all walks conducted with the Motorola Nexus 6. Our proposed methods
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clearly provide an enhancement for the overall localization process.}
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\caption{Error distribution of all walks conducted with the Motorola Nexus 6 for distinct percentile values.
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Our proposed methods clearly provide an enhancement for the overall localization process.
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\commentByFrank{percentile erwaehnt}}
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\label{fig:errorDistNexus}
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\end{figure}
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%\begin{figure}
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@@ -143,9 +149,11 @@
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%\end{figure}
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The median error values for all other paths and the other smartphone are listed in table
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\ref{tbl:errNexus} and \ref{tbl:errGalaxy}. As can be seen, adding prior knowledge
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is able to improve the localisation for all examined situations, even when
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\ref{tbl:errNexus} and \ref{tbl:errGalaxy}. Furthermore, fig. \ref{fig:errorDistNexus}
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depicts the error development for several percentile values. As can be seen, adding prior
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knowledge is able to improve the localisation for all examined situations, even when
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leaving the suggested path or when facing bad/slow sensor readings.
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\commentByFrank{fig. \ref{fig:errorDistNexus} erwaehnt}
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% error values
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\begin{table}
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