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added astar to smoothing transition

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toni
2016-04-11 10:57:29 +02:00
parent 5aad08e17c
commit de8783b9ca
13 changed files with 146 additions and 52 deletions
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2
code/CMakeLists.txt
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@@ -64,7 +64,7 @@ ADD_DEFINITIONS(
-fstack-protector-all -fstack-protector-all
-g -g
-O2 -O0
-DWITH_TESTS -DWITH_TESTS
-DWITH_ASSERTIONS -DWITH_ASSERTIONS

2
code/CMakeLists.txt.user
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@@ -1,6 +1,6 @@
<?xml version="1.0" encoding="UTF-8"?> <?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE QtCreatorProject> <!DOCTYPE QtCreatorProject>
<!-- Written by QtCreator 3.6.0, 2016-04-01T17:34:46. --> <!-- Written by QtCreator 3.6.0, 2016-04-11T10:29:08. -->
<qtcreator> <qtcreator>
<data> <data>
<variable>EnvironmentId</variable> <variable>EnvironmentId</variable>

3
code/DijkstraMapper.h
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@@ -26,6 +26,9 @@ public:
return d; return d;
} }
const std::vector<MyGridNode>& getAllNodes() const {return grid.getNodes();}
decltype(grid.neighbors(MyGridNode())) getNeighbors(const MyGridNode& node) const {return grid.neighbors(node);}
float getHeuristic(const MyGridNode& n1, const MyGridNode& n2) const {return std::abs(n1.x_cm - n2.x_cm) + std::abs(n1.y_cm - n2.y_cm);}
}; };
/** /**

1
code/MyGridNode.h
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@@ -21,6 +21,7 @@ struct MyGridNode : public GridNode, public GridPoint {
public: public:
/** needed ctor */ /** needed ctor */
MyGridNode() : GridNode(), GridPoint() {;}
MyGridNode(const float x_cm, const float y_cm, const float z_cm) : GridPoint(x_cm, y_cm, z_cm) {;} MyGridNode(const float x_cm, const float y_cm, const float z_cm) : GridPoint(x_cm, y_cm, z_cm) {;}
}; };

2
code/Settings.h
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@@ -17,7 +17,7 @@ namespace MiscSettings {
const int timeSteps = 500; const int timeSteps = 500;
const int numParticles = 7500; const int numParticles = 500;
const int lag = 5; const int lag = 5;

60
code/eval/FixedLagEvalBase.h
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@@ -414,44 +414,44 @@ public:
usleep(1000*33); usleep(1000*33);
// // append error for each run to a file // append error for each run to a file
// std::ofstream oTError("/tmp/errorsFilter.txt", std::ios_base::app); std::ofstream oTError("/tmp/errorsFilter.txt", std::ios_base::app);
// oTError << runName << "\n\t"; statsFiltering.appendTo(oTError); oTError << "\n\n"; oTError << runName << "\n\t"; statsFiltering.appendTo(oTError); oTError << "\n\n";
// oTError.close(); oTError.close();
// // append error for each run to a file // append error for each run to a file
// std::ofstream oSError("/tmp/errorsSmoothing.txt", std::ios_base::app); std::ofstream oSError("/tmp/errorsSmoothing.txt", std::ios_base::app);
// oSError << runName << "\n\t"; statsSmoothing.appendTo(oSError); oSError << "\n\n"; oSError << runName << "\n\t"; statsSmoothing.appendTo(oSError); oSError << "\n\n";
// oSError.close(); oSError.close();
// // plot-data // plot-data
// std::ofstream oPath("/tmp/path_" + runName + ".dat"); vis.groundTruth.addDataTo(oPath); oPath.close(); std::ofstream oPath("/tmp/path_" + runName + ".dat"); vis.groundTruth.addDataTo(oPath); oPath.close();
// std::ofstream oEst("/tmp/est_" + runName + ".dat"); vis.estPath.addDataTo(oEst); oEst.close(); std::ofstream oEst("/tmp/est_" + runName + ".dat"); vis.estPath.addDataTo(oEst); oEst.close();
// std::ofstream oFloor("/tmp/floors.dat"); vis.floors.addDataTo(oFloor); oFloor.close(); std::ofstream oFloor("/tmp/floors.dat"); vis.floors.addDataTo(oFloor); oFloor.close();
// std::ofstream oPlot("/tmp/plot_" + runName + ".gp"); std::ofstream oPlot("/tmp/plot_" + runName + ".gp");
// oPlot << "set terminal eps size 3.4,2\n"; oPlot << "set terminal eps size 3.4,2\n";
// oPlot << "set output '" << runName << ".eps'\n"; oPlot << "set output '" << runName << ".eps'\n";
// oPlot << "set termoption dashlength 0.5\n"; oPlot << "set termoption dashlength 0.5\n";
// oPlot << "set ticslevel 0\n"; oPlot << "set ticslevel 0\n";
// oPlot << "set view equal xy\n"; oPlot << "set view equal xy\n";
// oPlot << "set zrange [0:2200]\n"; oPlot << "set zrange [0:2200]\n";
// oPlot << "set multiplot layout 1,1 scale 2.7,2.7 offset 0,0.23\n"; oPlot << "set multiplot layout 1,1 scale 2.7,2.7 offset 0,0.23\n";
// oPlot << "set view 72,33\n"; oPlot << "set view 72,33\n";
// oPlot << "unset border\n"; oPlot << "unset border\n";
// oPlot << "unset xtics\n"; oPlot << "unset xtics\n";
// oPlot << "unset ytics\n"; oPlot << "unset ytics\n";
// oPlot << "unset ztics\n"; oPlot << "unset ztics\n";
// oPlot << "splot \\\n"; oPlot << "splot \\\n";
// oPlot << "'floors.dat' skip 21 notitle with lines lc rgb '#777777', \\\n"; oPlot << "'floors.dat' skip 21 notitle with lines lc rgb '#777777', \\\n";
// oPlot << "'path_bergwerk_path2_nexus_shortest.dat' skip 21 notitle with lines lw 2.5 dashtype 2 lc rgb '#007700', \\\n"; oPlot << "'path_bergwerk_path2_nexus_shortest.dat' skip 21 notitle with lines lw 2.5 dashtype 2 lc rgb '#007700', \\\n";
// oPlot << "'est_bergwerk_path2_nexus_shortest.dat' skip 21 notitle with lines lw 2.5 lc rgb '#000099' "; oPlot << "'est_bergwerk_path2_nexus_shortest.dat' skip 21 notitle with lines lw 2.5 lc rgb '#000099' ";
// oPlot.close(); oPlot.close();
} }
}; };

2
code/eval/SmoothingEval1.h
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@@ -51,7 +51,7 @@ public:
//create the backward smoothing filter //create the backward smoothing filter
bf = new K::BackwardSimulation<MyState>(500); bf = new K::BackwardSimulation<MyState>(50);
//bf = new K::CondensationBackwardFilter<MyState>; //bf = new K::CondensationBackwardFilter<MyState>;
bf->setSampler( std::unique_ptr<K::CumulativeSampler<MyState>>(new K::CumulativeSampler<MyState>())); bf->setSampler( std::unique_ptr<K::CumulativeSampler<MyState>>(new K::CumulativeSampler<MyState>()));

51
code/eval/SmoothingEvalBase.h
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@@ -184,7 +184,8 @@ public:
uint64_t lastTransitionTS = 0; uint64_t lastTransitionTS = 0;
int64_t start_time = -1; int64_t start_time = -1;
K::Statistics<double> stats; K::Statistics<double> statsFiltering;
K::Statistics<double> statsSmoothing;
int cnt = 0; int cnt = 0;
//stats file //stats file
@@ -273,8 +274,8 @@ public:
if (++cnt > 24) { if (++cnt > 24) {
pathEst.push_back(curEst); pathEst.push_back(curEst);
const float err = diff.length(); const float err = diff.length();
stats.add(err); statsFiltering.add(err);
std::cout << stats.asString() << std::endl; std::cout << statsFiltering.asString() << std::endl;
//save the current estimation for later smoothing. //save the current estimation for later smoothing.
pfHistory.push_back(pf->getNonResamplingParticles()); pfHistory.push_back(pf->getNonResamplingParticles());
@@ -321,7 +322,7 @@ public:
//File //File
//std::ofstream statsout2("/tmp/smoothed_" + runName + ".stats"); //std::ofstream statsout2("/tmp/smoothed_" + runName + ".stats");
stats.reset(); //stats.reset();
bf->reset(); bf->reset();
MyState estBF; MyState estBF;
@@ -345,8 +346,8 @@ public:
const Point3 diffSmoothed = curSmoothedEst - curGTSmoothed; const Point3 diffSmoothed = curSmoothedEst - curGTSmoothed;
const float errSmoothed = diffSmoothed.length(); const float errSmoothed = diffSmoothed.length();
stats.add(errSmoothed); statsSmoothing.add(errSmoothed);
std::cout << stats.asString() << std::endl; std::cout << statsSmoothing.asString() << std::endl;
// plot // plot
@@ -382,6 +383,44 @@ public:
//statsout2.close(); //statsout2.close();
// append error for each run to a file
std::ofstream oTError("/tmp/errorsFilter.txt", std::ios_base::app);
oTError << runName << "\n\t"; statsFiltering.appendTo(oTError); oTError << "\n\n";
oTError.close();
// append error for each run to a file
std::ofstream oSError("/tmp/errorsSmoothing.txt", std::ios_base::app);
oSError << runName << "\n\t"; statsSmoothing.appendTo(oSError); oSError << "\n\n";
oSError.close();
// plot-data
std::ofstream oPath("/tmp/path_" + runName + ".dat"); vis.groundTruth.addDataTo(oPath); oPath.close();
std::ofstream oEst("/tmp/est_" + runName + ".dat"); vis.estPath.addDataTo(oEst); oEst.close();
std::ofstream oFloor("/tmp/floors.dat"); vis.floors.addDataTo(oFloor); oFloor.close();
std::ofstream oPlot("/tmp/plot_" + runName + ".gp");
oPlot << "set terminal eps size 3.4,2\n";
oPlot << "set output '" << runName << ".eps'\n";
oPlot << "set termoption dashlength 0.5\n";
oPlot << "set ticslevel 0\n";
oPlot << "set view equal xy\n";
oPlot << "set zrange [0:2200]\n";
oPlot << "set multiplot layout 1,1 scale 2.7,2.7 offset 0,0.23\n";
oPlot << "set view 72,33\n";
oPlot << "unset border\n";
oPlot << "unset xtics\n";
oPlot << "unset ytics\n";
oPlot << "unset ztics\n";
oPlot << "splot \\\n";
oPlot << "'floors.dat' skip 21 notitle with lines lc rgb '#777777', \\\n";
oPlot << "'path_fixed_interval.dat' skip 21 notitle with lines lw 2.5 dashtype 2 lc rgb '#007700', \\\n";
oPlot << "'est_fixed_interval.dat' skip 21 notitle with lines lw 2.5 lc rgb '#000099' ";
oPlot.close();
} }
}; };

42
code/particles/smoothing/MySmoothingTransition.h
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@@ -7,6 +7,7 @@
#include <KLib/math/distribution/Uniform.h> #include <KLib/math/distribution/Uniform.h>
#include <Indoor/nav/dijkstra/Dijkstra.h> #include <Indoor/nav/dijkstra/Dijkstra.h>
#include <Indoor/nav/a-star/AStar.h>
#include <Indoor/grid/Grid.h> #include <Indoor/grid/Grid.h>
#include "../MyState.h" #include "../MyState.h"
@@ -22,6 +23,8 @@ static double smoothing_walk_sigma = 0.5;
static double smoothing_heading_sigma = 15.0; static double smoothing_heading_sigma = 15.0;
static double smoothing_baro_sigma = 0.2; static double smoothing_baro_sigma = 0.2;
typedef std::pair<const MyGridNode*, const MyGridNode*> my_key_type;
class MySmoothingTransition : public K::BackwardFilterTransition<MyState> { class MySmoothingTransition : public K::BackwardFilterTransition<MyState> {
private: private:
@@ -75,6 +78,7 @@ public:
// e.g. p(q_490(1)|q_489(1));p(q_490(1)|q_489(2)) ... p(q_490(1)|q_489(N)) and // e.g. p(q_490(1)|q_489(1));p(q_490(1)|q_489(2)) ... p(q_490(1)|q_489(N)) and
// p(q_490(1)|q_489(1)); p(q_490(2)|q_489(1)) ... p(q_490(M)|q_489(1)) // p(q_490(1)|q_489(1)); p(q_490(2)|q_489(1)) ... p(q_490(M)|q_489(1))
std::vector<std::vector<double>> predictionProbabilities; std::vector<std::vector<double>> predictionProbabilities;
std::map<my_key_type, double> shortestPathMap;
auto p1 = particles_old.begin(); auto p1 = particles_old.begin();
auto p2 = particles_new.begin(); auto p2 = particles_new.begin();
@@ -94,12 +98,40 @@ public:
const MyGridNode* dst = grid->getNodePtrFor(GridPoint(p1->state.pCur.x, p1->state.pCur.y, p1->state.pCur.z)); const MyGridNode* dst = grid->getNodePtrFor(GridPoint(p1->state.pCur.x, p1->state.pCur.y, p1->state.pCur.z));
const MyGridNode* src = grid->getNodePtrFor(GridPoint(p2->state.pCur.x, p2->state.pCur.y, p2->state.pCur.z)); const MyGridNode* src = grid->getNodePtrFor(GridPoint(p2->state.pCur.x, p2->state.pCur.y, p2->state.pCur.z));
Dijkstra<MyGridNode> dijkstra; // Dijkstra<MyGridNode> dijkstra;
dijkstra.build(src, dst, DijkstraMapper(*grid)); // dijkstra.build(src, dst, DijkstraMapper(*grid));
double distDijkstra_m = dijkstra.getNode(*src)->cumWeight; // double distDijkstra_m = dijkstra.getNode(*src)->cumWeight;
const double distProb = distWalk.getProbability(distDijkstra_m); AStar<MyGridNode> aStar;
DijkstraMapper dm(*grid);
double distDijkstra_m = 0;
std::vector<const MyGridNode*> shortestPath;
// check if this shortestPath was already calculated
std::map<my_key_type, double>::iterator it;
it = shortestPathMap.find(my_key_type(dst, src));
if(it != shortestPathMap.end()){
distDijkstra_m = it->second;
}
else{
//Dijkstra/A* for shortest path
shortestPath = aStar.get(src, dst, dm);
//get distance walked and getProb using the walking model
for(int i = 0; i < shortestPath.size() - 1; ++i){
distDijkstra_m += dm.getWeightBetween(*shortestPath[i], *shortestPath[i+1]);
}
if(distDijkstra_m != distDijkstra_m) {throw "detected NaN";}
//save distance and nodes in lookup map
#pragma omp critical
shortestPathMap.insert(std::make_pair(my_key_type(dst, src), distDijkstra_m));
}
const double distProb = distWalk.getProbability(distDijkstra_m * 0.01);
//getProb using the angle(heading) between src and dst //getProb using the angle(heading) between src and dst
// double angle = 0.0; // double angle = 0.0;
@@ -127,7 +159,7 @@ public:
//if(distance_m != distance_m) {throw "detected NaN";} //if(distance_m != distance_m) {throw "detected NaN";}
//if(distProb != distProb) {throw "detected NaN";} //if(distProb != distProb) {throw "detected NaN";}
if(angle != angle) {throw "detected NaN";} //if(angle != angle) {throw "detected NaN";}
if(headingProb != headingProb) {throw "detected NaN";} if(headingProb != headingProb) {throw "detected NaN";}
if(floorProb != floorProb) {throw "detected NaN";} if(floorProb != floorProb) {throw "detected NaN";}
if(floorProb == 0) {throw "detected NaN";} if(floorProb == 0) {throw "detected NaN";}

BIN
tex/bare_conf.dvi
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2
tex/bare_conf.tex
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@@ -137,7 +137,7 @@
% not capitalized unless they are the first or last word of the title. % not capitalized unless they are the first or last word of the title.
% Linebreaks \\ can be used within to get better formatting as desired. % Linebreaks \\ can be used within to get better formatting as desired.
% Do not put math or special symbols in the title. % Do not put math or special symbols in the title.
\title{On Prior Navigation Knowledge in Multi Sensor Indoor Localisation} \title{On Monte Carlo Smoothing in Multi Sensor Indoor Localisation}
% author names and affiliations % author names and affiliations

14
tex/chapters/experiments.tex
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@@ -1,3 +1,17 @@
\section{Experiments} \section{Experiments}
ddd \cite{Ville09} dddd ddd \cite{Ville09} dddd
\begin{itemize}
\item Vorwärtsschritt die Ergebnisse und Probleme beschreiben. Zeitlicher Verzug etc.
\item Fixed-Interval Smoothing mit beiden Methoden. Was wird besser? Warum?
\subitem AUf die schlechte Performance eingehen für was könnte man es nutzen?
\item Fixed-lag Smoothing der beiden Methoden
\subitem erstmal ergebnisse mit 3 unterschiedlichen lags. 5, 30, 50 oder so
\subitem Fehlerplot wie beim fusion paper. wo ist der error besonders hoch, warum? warum wurde er besser?
\subitem welche anwendungen gibt es da jeweils?
\subitem plot mit kurven die den fehler bei den unterschiedlichen lag längen angibt.
\item Fixed-lag gap
\subitem einen offset (gap) im smoothing. was bringt es? sinnvoll?
\end{itemize}

7
tex/chapters/introduction.tex
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@@ -1,5 +1,10 @@
\section{Introduction} \section{Introduction}
Smoothing Smoothing Smoothing Es gibt viele unterschiedliche Indoor Lösungen die auf blabalbal und blabal aufbauen. particle filter ist etabliert und fast alle nutzen einen. gibt solche und solche methoden. aber alle haben mehr oder wenig ähnliche probleme und herrausforderungen. eine generelle lösunge solceh herrausforderungen zu meistern sind smoothiner methoden.
probleme: multimodalitäten. zeitlicher verzug. falsche messungen. etc etc. all das kann mit wissen aus zukünftigen messungen behoben werden.