deactivated comments from frank
This commit is contained in:
toni
@@ -42,7 +42,7 @@
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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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%\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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@@ -66,7 +66,7 @@
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\caption{The four paths that were part of the evaluation.
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Starting positions are marked with black circles.
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For a better visualisation they were slightly shifted to avoid overlapping.}
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\commentByFrank{font war korrekt, aber die groesse war zu gross im vgl. zu den anderen}
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%\commentByFrank{font war korrekt, aber die groesse war zu gross im vgl. zu den anderen}
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\label{fig:paths}
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\end{figure}
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% error development over time while walking along a path
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@@ -76,7 +76,7 @@
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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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%\commentByFrank{hilft das bold vlt. schon um die legende zu verstehen?}
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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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@@ -138,7 +138,7 @@
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\input{gfx/eval/error_dist_nexus}
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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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%\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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@@ -151,7 +151,7 @@
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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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%\commentByFrank{fig. \ref{fig:errorDistNexus} erwaehnt}
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% error values
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\begin{table}
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@@ -8,7 +8,7 @@
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To sample only transitions that are actually feasible
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within the environment, we utilize a \SI{20}{\centimeter}-gridded graph
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$G = (V,E)$ with vertices $v_i \in V$ and undirected edges $e_{i,j} \in E$
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\commentByFrank{notation geaendert. so ok?}
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%\commentByFrank{notation geaendert. so ok?}
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derived from the buildings floorplan as described in section \ref{sec:relatedWork}.
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However, we add improved $z$-transitions by also modelling realistic
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stairwells using nodes and edges, depicted in fig. \ref{fig:gridStairs}.
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@@ -36,7 +36,7 @@
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New states $\mStateVec_{t}$ may now be sampled by starting at the vertex for
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position $\fPos{\mStateVec_{t-1}} = (x,y,z)^T$
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\commentByFrank{eingefuehrt}
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%\commentByFrank{eingefuehrt}
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and walking along adjacent nodes into a given walking-direction $\gHead$ until a distance $\gDist$ is
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reached \cite{Ebner-15}.
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Both, heading and distance, are supplied by the current sensor readings $\mObsVec_{t}$
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@@ -48,21 +48,21 @@
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\gDist &= \mObs_t^{\mObsSteps} \cdot \mStepSize + \mathcal{N}(0, \sigma_{\gDist}^2)
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.
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\end{align}
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\commentByFrank{fixed. war das falsche makro in (2) und dem satz darunter. das delta musste weg. der state hat ein absolutes heading. step-size als variable}
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%\commentByFrank{fixed. war das falsche makro in (2) und dem satz darunter. das delta musste weg. der state hat ein absolutes heading. step-size als variable}
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%
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During the random walk, each edge has its own probability $p(\mEdgeAB)$
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which e.g. depends on the edge's direction $\angle \mEdgeAB$ and the
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pedestrian's current heading $\gHead$.
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Furthermore, section \ref{sec:nav} uses $p(\mEdgeAB)$ to incorporate prior path knowledge to
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favour edges leading towards the pedestrian's desired target $\mVertexDest$.
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\commentByFrank{fixed}
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%\commentByFrank{fixed}
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For each single movement on the graph, we calculate $p(\mEdgeAB)$ for all edges
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connected to a vertex $\mVertexA$, and, hereafter, randomly draw the to-be-walked edge
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depending on those probabilities. This step is repeated until the sum
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of the length of all used edges exceeds $d$. The latter depends on the number of
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detected steps $\mObs_t^{\mObsSteps}$ and the pedestrian's step-size $\mStepSize$.
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\commentByFrank{step-size als variable}
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%\commentByFrank{step-size als variable}
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To quantify the improvement prior knowledge is able to provide,
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we define a simple reference for $p(\mEdgeAB)$ that assigns a probability to each edge
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@@ -105,7 +105,7 @@
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themselves and nearby walls. To calculate paths that resemble this behaviour, an importance-factor is derived for
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each vertex. Those will be used to scale the distance between two nodes, just like navigation systems use
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the speed-limit as scaling-factor.
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\commentByFrank{so besser? der ganze absatz.}
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%\commentByFrank{so besser? der ganze absatz.}
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To downvote vertices near walls, we need to determine the distance of each vertex from its nearest wall.
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We therefore derive an inverted version $G' = (V', E')$ of the graph $G$, just describing walls and
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obstacles. A nearest-neighbour search \cite{Cover1967} $\fNN{\mVertexA}{V'}$ within $V'$ provides the vertex
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@@ -121,7 +121,7 @@
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\enskip .
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\label{eq:wallAvoidance}
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\end{equation}
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\commentByFrank{fixed. WA war WallAvoidance. hatte statt ll immer $\|$ gelesen und deshalb nicht verstanden}
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%\commentByFrank{fixed. WA war WallAvoidance. hatte statt ll immer $\|$ gelesen und deshalb nicht verstanden}
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%
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%The parameters of the normal distribution and the scaling-factors were chosen empirically.
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%While this approach provides good results for most areas, doors are downvoted by
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@@ -155,7 +155,7 @@
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\end{equation}
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%
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For $\mat{\Sigma}$, the two largest eigenvalues $\lambda_1, \lambda_2$ with $\lambda_1 > \lambda_2$
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\commentByFrank{fixed}
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%\commentByFrank{fixed}
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are calculated. If their ratio $^{\lambda_1}/_{\lambda_2}$ is above a certain
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threshold, the neighbourhood describes a flat ellipse and thus either a door or a straight wall.
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%
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@@ -180,7 +180,7 @@
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the distance of a vertex $\mVertexA$ from its nearest door and a deviation
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of \SI{1.0}{\meter}:
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%
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%\commentByFrank{distanzrechnung: formel ok?}
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%%\commentByFrank{distanzrechnung: formel ok?}
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\begin{equation}
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\fDD{\mVertexA} = \mathcal{N}( \| \fPos{\mVertexA} - \vec{c} \| \mid 0.0, 1.0^2 )
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\label{eq:doorDetection}
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@@ -265,7 +265,7 @@
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represents the most proper state of the posterior distribution at time $t-1$, is calculated.
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%
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%
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%\commentByFrank{avg-state vom sample-set. frank d. meinte ja hier muessen wir aufpassen. bin noch unschluessig wie.}
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%%\commentByFrank{avg-state vom sample-set. frank d. meinte ja hier muessen wir aufpassen. bin noch unschluessig wie.}
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%\commentByToni{Das ist gar nicht so einfach... wir haben nie ein Sample Set eingefuehrt. Nicht mal einen Sample. Wir haben immer nur diesen State... Man könnte natuerlich einfach sagen das $\Upsilon_t$ an set of random samples representing the posterior distribution ist oder einfach nur ein set von partikeln. habs mal eingefuegt wie ich denke}
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%
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This centre serves as the starting point for the shortest-path calculation.
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@@ -279,7 +279,7 @@ represents the most proper state of the posterior distribution at time $t-1$, is
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%
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We thus calculate the standard deviation of the distance of all sample-positions
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$\fPos{\mStateVec_{t-1}}$ from aforementioned centre $\pathCentroid$.
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\commentByFrank{so klarer? platz fuer groese Eq. fehlt und Notation zum ansprechen jedes einzelnen Particles vermeide ich lieber...}
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%\commentByFrank{so klarer? platz fuer groese Eq. fehlt und Notation zum ansprechen jedes einzelnen Particles vermeide ich lieber...}
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%\begin{equation}
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% d_\text{cen} = \| pos(q_{t-1}) - \pathCentroid \|
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@@ -309,7 +309,7 @@ represents the most proper state of the posterior distribution at time $t-1$, is
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\enskip .
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\label{eq:transShortestPath}
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\end{equation}
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\commentByFrank{$\mUsePath$ als variable}
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%\commentByFrank{$\mUsePath$ als variable}
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%
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@@ -348,7 +348,7 @@ represents the most proper state of the posterior distribution at time $t-1$, is
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Both possible paths are covered and slight deviations are possible.
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Additionally shows the shortest-path calculation without (dashed) and with (solid) importance-factors
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used for edge-weight-adjustment.}
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\commentByFrank{so besser?}
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%\commentByFrank{so besser?}
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\label{fig:multiHeatMap}
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\end{figure}
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@@ -19,7 +19,7 @@
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\caption{Sometimes the smartphone's barometer (here: Motorola Nexus 6) provides erroneous pressure readings
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during the first seconds. Those need to be omitted before $\sigma_\text{baro}$ and
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$\overline{\mObsPressure}$ are estimated.}
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\commentByFrank{fixed}
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%\commentByFrank{fixed}
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\label{fig:baroSetupError}
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\end{figure}
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%
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