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@@ -77,6 +77,8 @@ Now, the positional error along all 4 paths could be improved from \SI{}{} to \S
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However, BS still outperforms the FBS by an average of \SI{}{} on all 4 paths using the same number of particles and \SI{500}{} sample realisations.
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A visual example comparing both smoothing methods on path 4 is illustrated in fig. \ref{fig:intcomp}.
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The estimation of BS (blue) looks way more realistic and adapts better to the ground truth path.
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However, in this particular example the FBS (red) starts at an earlier position, better handling the initial uniform distribution.
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Another advantage of BS over FBS, is the ability to still improve the filtering results even while reducing the number of particles radical.
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For example \SI{50}{} particles and \SI{25}{} sample realisations are providing reliable estimations similar to above experiments, though the risk of losing track is higher.
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\begin{figure}
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@@ -103,6 +105,18 @@ Especially interesting in this context are small lags $\tau < 10$ considering fi
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%wie gut ist fixed-lag mit einem lag = 5. was fällt so auf?
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Fig. \ref{} illustrates the estimation results for path 4 using \SI{2500}{particles}, \SI{500}{sample realisations} for BS and a fixed-lag $\tau = 5$.
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It can be seen that again BS provides a better overall estimation, especially in areas where the user is changing floors.
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Besides the positional quality, also the timely error could be reduced by both algorithms using this fixed-lag.
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Once more, the BS outperforms the FBS by providing an overall approximation error of $\SI{55}{\centimeter}$ by filtering with $\SI{55}{\centimeter}$, while FBS improves to $\SI{55}{\centimeter}$.
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Besides changing the number of particles, it is also possible the variate the lag.
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As one would expect, increasing the lag causes the smoothed estimation to approach the results provided by fixed-interval smoothing.
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This can be verified by looking at fig. \ref{}, which is a detailed view of segment XX in fig. \ref{}.
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It is obvious that a lag of \SI{30}{} time steps has access to much more future observations and is therefore able to obtain such a result.
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Considering an update interval of \SI{500}{\milli\second}, a lag of \SI{30}{} would however mean that the smoother is \SI{15}{\second} behind the filter.
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Nevertheless, there are practical applications like accurately verifying hit checkpoints or
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\todo{Experimente noch etwas theoretisch verfeinert. Nicht nur bloße beobachtungen}
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%lag vergrößern was passiert beschreiben
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