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IPIN2017/code/main.cpp
2017-03-30 18:52:49 +02:00

390 lines
15 KiB
C++
Executable File

#include <iostream>
#include "filter/Structs.h"
#include "filter/KLB.h"
#include "Plotti.h"
#include "filter/Logic.h"
#include "Settings.h"
//frank
//const std::string mapDir = "/mnt/data/workspaces/IPIN2016/IPIN2016/competition/maps/";
//const std::string dataDir = "/mnt/data/workspaces/IPIN2016/IPIN2016/competition/src/data/";
//toni
const std::string mapDir = "/home/toni/Documents/programme/localization/IndoorMap/maps/";
const std::string dataDir = "/home/toni/Documents/programme/localization/IPIN2017/code/data/";
const std::string errorDir = dataDir + "results/";
/** describes one dataset (map, training, parameter-estimation, ...) */
struct DataSetup {
std::string map;
std::vector<std::string> training;
std::string wifiParams;
int minWifiOccurences;
VAPGrouper::Mode vapMode;
std::string grid;
};
/** all configured datasets */
struct Data {
DataSetup BERKWERK = {
mapDir + "SHL/SHL25.xml",
{
dataDir + "bergwerk/path1/nexus/vor/1454775984079.csv",
dataDir + "bergwerk/path1/galaxy/vor/1454776168794.csv",
dataDir + "bergwerk/path2/nexus/vor/1454779863041.csv",
dataDir + "bergwerk/path2/galaxy/vor/1454780113404.csv",
dataDir + "bergwerk/path3/nexus/vor/1454782562231.csv",
dataDir + "bergwerk/path3/galaxy/vor/1454782896548.csv",
dataDir + "bergwerk/path4/nexus/vor/1454776525797.csv",
dataDir + "bergwerk/path4/galaxy/vor/1454779020844.csv"
},
dataDir + "bergwerk/wifiParams.txt",
40,
VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
mapDir + "SHL/grid25.dat"
};
DataSetup IPIN2015 = {
mapDir + "SHL/SHL_IPIN2015_gt.xml",
{
dataDir + "ipin2015/galaxy/Path0/1433581471902.csv",
dataDir + "ipin2015/nexus/Path0/1433606195078.csv",
dataDir + "ipin2015/galaxy/Path1/1433587749492.csv",
dataDir + "ipin2015/nexus/Path1/1433606670723.csv", // wlan für 71 sekunden weg. verlaufen uns aufm klo.
dataDir + "ipin2015/galaxy/Path2/1433581471902.csv",
dataDir + "ipin2015/nexus/Path2/1433607251262.csv",
dataDir + "eiszeit/path2/1479986737368.csv"
},
dataDir + "bergwerk/wifiParams.txt",
40,
VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
mapDir + "SHL/grid_IPIN2015_gt.dat"
};
DataSetup IPIN2017 = {
mapDir + "SHL38.xml",
{
dataDir + "ipin2017/nogps/i-building/path1/1489769326868.csv",
dataDir + "ipin2017/nogps/i-building/path1/1489769510080.csv",
dataDir + "ipin2017/nogps/i-building/path2/1489774173022.csv",
dataDir + "ipin2017/nogps/i-building/path2/1489774361865.csv",
dataDir + "ipin2017/nogps/i-building/path3/1489776812891.csv",
dataDir + "ipin2017/nogps/i-building/path3/1489776979143.csv",
dataDir + "ipin2017/nogps/all/path1/1490031549543.csv",
dataDir + "ipin2017/nogps/all/path1/1490031883742.csv",
dataDir + "ipin2017/nogps/all/path2/1490032575999.csv",
dataDir + "ipin2017/nogps/all/path2/1490032861864.csv",
dataDir + "ipin2017/nogps/all/path3/EMPTY.csv",
dataDir + "ipin2017/nogps/all/path3/EMPTY.csv",
},
mapDir + "wifi_fp_all.dat",
40,
VAPGrouper::Mode::LAST_MAC_DIGIT_TO_ZERO,
mapDir + "grid_SHL38.dat"
};
} data;
Floorplan::IndoorMap* MyState::map;
void run(DataSetup setup, int numFile, std::string folder, std::vector<int> gtPath) {
std::vector<double> kld_data;
// load the floorplan
Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(setup.map);
MyState::map = map;
WiFiModelLogDistCeiling WiFiModel(map);
WiFiModel.loadAPs(map, Settings::WiFiModel::TXP, Settings::WiFiModel::EXP, Settings::WiFiModel::WAF);
Assert::isFalse(WiFiModel.getAllAPs().empty(), "no AccessPoints stored within the map.xml");
BeaconModelLogDistCeiling beaconModel(map);
beaconModel.loadBeaconsFromMap(map, Settings::BeaconModel::TXP, Settings::BeaconModel::EXP, Settings::BeaconModel::WAF);
//Assert::isFalse(beaconModel.getAllBeacons().empty(), "no Beacons stored within the map.xml");
// build the grid
std::ifstream inp(setup.grid, std::ifstream::binary);
Grid<MyNode> grid(20);
// grid.dat empty? -> build one and save it
if (!inp.good() || (inp.peek()&&0) || inp.eof()) {
std::ofstream onp;
onp.open(setup.grid);
GridFactory<MyNode> factory(grid);
factory.build(map);
grid.write(onp);
} else {
grid.read(inp);
}
// add node-importance
Importance::addImportance(grid);
// stamp WiFi signal-strengths onto the grid
WiFiGridEstimator::estimate(grid, WiFiModel, Settings::smartphoneAboveGround);
// reading file
Offline::FileReader fr(setup.training[numFile]);
//interpolator for ground truth
Interpolator<uint64_t, Point3> gtInterpolator = fr.getGroundTruthPath(map, gtPath);
//gnuplot plot
Plotti plot;
plot.addFloors(map);
plot.addOutline(map);
plot.addStairs(map);
plot.gp << "set autoscale xy\n";
//plot.addGrid(grid);
// init ctrl and observation
MyControl ctrl;
ctrl.resetAfterTransition();
MyObs obs;
//random start position
std::unique_ptr<K::ParticleFilterInitializer<MyState>> init(new PFInit(grid)); std::move(init);
//filter init
//std::unique_ptr<PFInit> init =
K::ParticleFilterHistory<MyState, MyControl, MyObs> pf(Settings::numParticles, std::unique_ptr<PFInit>(new PFInit(grid)));
//K::ParticleFilterHistory<MyState, MyControl, MyObs> pf(Settings::numParticles, std::unique_ptr<PFInitFixed>(new PFInitFixed(grid, GridPoint(1120.0f, 750.0f, 740.0f), 90.0f)));
pf.setTransition(std::unique_ptr<PFTrans>(new PFTrans(grid, &ctrl)));
pf.setEvaluation(std::unique_ptr<PFEval>(new PFEval(WiFiModel, beaconModel, grid)));
//resampling
//pf.setResampling(std::unique_ptr<K::ParticleFilterResamplingSimple<MyState>>(new K::ParticleFilterResamplingSimple<MyState>()));
//pf.setResampling(std::unique_ptr<K::ParticleFilterResamplingPercent<MyState>>(new K::ParticleFilterResamplingPercent<MyState>(0.4)));
//pf.setResampling(std::unique_ptr<NodeResampling<MyState, MyNode>>(new NodeResampling<MyState, MyNode>(*grid)););
pf.setResampling(std::unique_ptr<K::ParticleFilterResamplingDivergence<MyState>>(new K::ParticleFilterResamplingDivergence<MyState>()));
pf.setNEffThreshold(0.95);
//estimation
//pf.setEstimation(std::unique_ptr<K::ParticleFilterEstimationWeightedAverage<MyState>>(new K::ParticleFilterEstimationWeightedAverage<MyState>()));
//pf.setEstimation(std::unique_ptr<K::ParticleFilterEstimationRegionalWeightedAverage<MyState>>(new K::ParticleFilterEstimationRegionalWeightedAverage<MyState>()));
pf.setEstimation(std::unique_ptr<K::ParticleFilterEstimationOrderedWeightedAverage<MyState>>(new K::ParticleFilterEstimationOrderedWeightedAverage<MyState>(0.5)));
//pf.setEstimation(std::unique_ptr<K::ParticleFilterEstimationKernelDensity<MyState, 3>>(new K::ParticleFilterEstimationKernelDensity<MyState, 3>()));
Timestamp lastTimestamp = Timestamp::fromMS(0);
StepDetection sd;
TurnDetection td;
MotionDetection md;
RelativePressure relBaro; relBaro.setCalibrationTimeframe( Timestamp::fromMS(5000) );
K::Statistics<float> errorStats;
//calc wi-fi prob for every node and get mean vector
WiFiObserverFree wiFiProbability(Settings::WiFiModel::sigma, WiFiModel);
//file writing for error data
long int t = static_cast<long int>(time(NULL));
std::ofstream errorFile;
errorFile.open (errorDir + folder + "/error_" + std::to_string(numFile) + "_" + std::to_string(t) + ".csv");
// parse each sensor-value within the offline data
for (const Offline::Entry& e : fr.getEntries()) {
const Timestamp ts = Timestamp::fromMS(e.ts);
if (e.type == Offline::Sensor::WIFI) {
obs.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
} else if (e.type == Offline::Sensor::BEACON){
obs.beacons.entries.push_back(fr.getBeacons()[e.idx].data);
// remove to old beacon measurements
obs.beacons.removeOld(ts);
} else if (e.type == Offline::Sensor::ACC) {
if (sd.add(ts, fr.getAccelerometer()[e.idx].data)) {
++ctrl.numStepsSinceLastTransition;
}
const Offline::TS<AccelerometerData>& _acc = fr.getAccelerometer()[e.idx];
td.addAccelerometer(ts, _acc.data);
} else if (e.type == Offline::Sensor::GYRO) {
const Offline::TS<GyroscopeData>& _gyr = fr.getGyroscope()[e.idx];
const float delta_gyro = td.addGyroscope(ts, _gyr.data);
ctrl.turnSinceLastTransition_rad += delta_gyro;
} else if (e.type == Offline::Sensor::BARO) {
relBaro.add(ts, fr.getBarometer()[e.idx].data);
obs.relativePressure = relBaro.getPressureRealtiveToStart();
obs.sigmaPressure = relBaro.getSigma();
} else if (e.type == Offline::Sensor::LIN_ACC) {
md.addLinearAcceleration(ts, fr.getLinearAcceleration()[e.idx].data);
} else if (e.type == Offline::Sensor::GRAVITY) {
md.addGravity(ts, fr.getGravity()[e.idx].data);
Eigen::Vector2f curVec = md.getCurrentMotionAxis();
ctrl.motionDeltaAngle_rad = md.getMotionChangeInRad();
}
if (ts.ms() - lastTimestamp.ms() > 500) {
obs.currentTime = ts;
const WiFiMeasurements wifiObs = Settings::WiFiModel::vg_eval.group(obs.wifi);
std::vector<MyNode> allNodes = grid.getNodes();
std::vector<K::Particle<MyState>> particleWifi;
for(MyNode node : allNodes){
double prob = wiFiProbability.getProbability(node, ts, wifiObs);
K::Particle<MyState> tmp (MyState(GridPoint(node.x_cm, node.y_cm, node.z_cm)), prob);
particleWifi.push_back(tmp);
}
if(kld_data.empty()){
kld_data.push_back(0.0);
}
std::function<double(std::vector<K::Particle<MyState>>&, MyState, std::vector<K::Particle<MyState>>&)> kldFunc = getKernelDensityProbability;
//std::function<double(std::vector<K::Particle<MyState>>&, MyState, std::vector<K::Particle<MyState>>&)> kldFunc = kldFromMultivariatNormal;
double kld = 0.0;
MyState est = pf.update(&ctrl, obs, particleWifi, kldFunc, kld);
Point3 estPos = est.position.inMeter();
//double kld = getKernelDensityProbability(pf, WiFiModel, obs, grid, ts, plot);
//double kld = kldFromMultivariatNormal(pf, estPos, particleWifi, plot);
kld_data.push_back(kld);
//current ground truth position
Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms()));
/** plotting stuff */
plot.pInterest.clear();
//turn angle plot
static float angleSumTurn = 0; angleSumTurn += ctrl.turnSinceLastTransition_rad;
plot.showAngle(1, angleSumTurn + M_PI, Point2(0.9, 0.9), "Turn: ");
//motion angle plot
static float angleSumMotion = 0; angleSumMotion += ctrl.motionDeltaAngle_rad;
plot.showAngle(2, angleSumMotion + M_PI, Point2(0.9, 0.8), "Motion: ");
plot.setEst(estPos);
plot.setGT(gtPos);
plot.addEstimationNode(estPos);
plot.addParticles(pf.getParticles());
//plot.gp << "set arrow 919 from " << tt.pos.x << "," << tt.pos.y << "," << tt.pos.z << " to "<< tt.pos.x << "," << tt.pos.y << "," << tt.pos.z+1 << "lw 3\n";
//plot.gp << "set label 1001 at screen 0.02, 0.98 'base:" << relBaro.getBaseAvg() << " sigma:" << relBaro.getSigma() << " cur:" << relBaro.getPressureRealtiveToStart() << " hPa " << -relBaro.getPressureRealtiveToStart()/0.10/4.0f << " floor'\n";
int minutes = static_cast<int>(ts.sec()) / 60;
plot.gp << "set label 1002 at screen 0.02, 0.94 'Time: " << minutes << ":" << static_cast<int>(static_cast<int>(ts.sec())%60) << "'\n";
plot.gp << "set label 1002 at screen 0.04, 0.94 'KLD: " << ":" << kld << "'\n";
//plot.gp << "set label 1002 at screen 0.98, 0.98 'act:" << ctrl.barometer.act << "'\n";
// error between GT and estimation
float err_m = gtPos.getDistance(estPos);
errorStats.add(err_m);
errorFile << err_m << "\n";
plot.show();
usleep(10*10);
lastTimestamp = ts;
// reset control
ctrl.resetAfterTransition();
}
}
errorFile.close();
std::cout << "Statistical Analysis: " << std::endl;
std::cout << "Median: " << errorStats.getMedian() << " Average: " << errorStats.getAvg() << std::endl;
//Write the current plotti buffer into file
std::ofstream plotFile;
plotFile.open(errorDir + std::to_string(numFile) + "_" + std::to_string(t) + ".gp");
plot.saveToFile(plotFile);
plotFile.close();
for(int i = 0; i < map->floors.size(); ++i){
plot.printSingleFloor("/home/toni/Documents/programme/localization/IPIN2017/code/eval/"+ folder + "/image" + std::to_string(numFile) + "_" + std::to_string(t), i);
plot.show();
usleep(10*10);
}
plot.printSideView("/home/toni/Documents/programme/localization/IPIN2017/code/eval/"+ folder + "/image" + std::to_string(numFile) + "_" + std::to_string(t), 90);
plot.show();
plot.printSideView("/home/toni/Documents/programme/localization/IPIN2017/code/eval/"+ folder + "/image" + std::to_string(numFile) + "_" + std::to_string(t), 0);
plot.show();
plot.printOverview("/home/toni/Documents/programme/localization/IPIN2017/code/eval/"+ folder + "/image" + std::to_string(numFile) + "_" + std::to_string(t));
plot.show();
//draw kld
K::Gnuplot gp;
K::GnuplotPlot plotkld;
K::GnuplotPlotElementLines lines;
//save as screenshot
std::string path = "/home/toni/Documents/programme/localization/IPIN2017/code/eval/"+ folder + "/image" + std::to_string(numFile) + "_" + std::to_string(t);
gp << "set terminal png size 1280,720\n";
gp << "set output '" << path << "_shennendistance.png'\n";
for(int i=0; i < kld_data.size()-1; ++i){
K::GnuplotPoint2 p1(i, kld_data[i]);
K::GnuplotPoint2 p2(i+1, kld_data[i+1]);
lines.addSegment(p1, p2);
}
plotkld.add(&lines);
gp.draw(plotkld);
gp.flush();
std::cout << "finished" << std::endl;
sleep(1);
}
int main(int argc, char** argv) {
//Testing files
//run(data.BERKWERK, 6, "EVALBERGWERK"); // Nexus vor
//for(int i = 0; i < 5; ++i){
//run(data.IPIN2017, 0, "ipin2017"); // Nexus Path2
//run(data.IPIN2017, 1, "ipin2017");
run(data.IPIN2017, 4, "ipin2017", Settings::Paths_IPIN2017::path3);
run(data.IPIN2017, 2, "ipin2017", Settings::Paths_IPIN2017::path2);
run(data.IPIN2017, 5, "ipin2017", Settings::Paths_IPIN2017::path3);
run(data.IPIN2017, 3, "ipin2017", Settings::Paths_IPIN2017::path2);
//}
}