575 lines
20 KiB
C++
575 lines
20 KiB
C++
#include "main.h"
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#include "mesh.h"
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#include "filter.h"
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#include "Settings.h"
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#include "meshPlotter.h"
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#include "Plotty.h"
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#include <array>
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#include <memory>
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#include <thread>
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#include <filesystem>
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#include <chrono>
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#include <Indoor/floorplan/v2/FloorplanReader.h>
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#include <Indoor/sensors/offline/FileReader.h>
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#include <Indoor/geo/Heading.h>
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#include <Indoor/geo/Point2.h>
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#include <Indoor/sensors/imu/TurnDetection.h>
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#include <Indoor/sensors/imu/StepDetection.h>
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#include <Indoor/sensors/imu/PoseDetection.h>
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#include <Indoor/sensors/imu/MotionDetection.h>
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#include <Indoor/sensors/pressure/RelativePressure.h>
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#include <Indoor/data/Timestamp.h>
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#include <Indoor/math/stats/Statistics.h>
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#include "FtmKalman.h"
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#include "mainFtm.h"
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#include "misc.h"
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#include <sys/stat.h>
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using namespace std::chrono_literals;
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std::vector<std::tuple<float, float, float>> getFtmValues(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator, const MACAddress nuc)
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{
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std::vector<std::tuple<float, float, float>> result;
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for (const Offline::Entry& e : fr.getEntries())
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{
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if (e.type == Offline::Sensor::WIFI_FTM)
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{
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const Timestamp ts = Timestamp::fromMS(e.ts);
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Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0, 0, 1.3);
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auto wifi = fr.getWifiFtm()[e.idx].data;
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if (wifi.getAP().getMAC() == nuc)
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{
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Point3 apPos = Settings::data.CurrentPath.NUCs.find(wifi.getAP().getMAC())->second.position;
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float apDist = gtPos.getDistance(apPos);
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float ftmDist = wifi.getFtmDist();
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float rssi = wifi.getRSSI();
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result.push_back({ apDist, ftmDist, rssi });
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}
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}
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}
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return result;
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}
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std::pair<float, float> optimizeFtm(std::vector<std::tuple<float, float, float>>& values)
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{
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std::vector<std::pair<float, float>> error;
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for (float offset = 0; offset < 10.0f; offset += 0.25)
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{
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Stats::Statistics<float> diffs;
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for (const auto& data : values)
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{
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float apDist = std::get<0>(data);
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float ftmDist = std::get<1>(data);
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ftmDist += offset;
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float diff = (apDist - ftmDist);
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diffs.add(diff);
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}
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error.push_back({ offset, diffs.getSquaredSumAvg() });
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}
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auto minElement = std::min_element(error.begin(), error.end(), [](std::pair<float, float> a, std::pair<float, float> b) {
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return a.second < b.second;
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});
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std::cout << "Min ftm offset \t" << minElement->first << "\t" << minElement->second << "\n";
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return *minElement;
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}
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std::pair<float, float> optimizeRssi(std::vector<std::tuple<float, float, float>>& values)
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{
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std::vector<std::pair<float, float>> error;
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for (float pathLoss = 2.0f; pathLoss < 4.0f; pathLoss += 0.125)
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{
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Stats::Statistics<float> diffs;
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for (const auto& data : values)
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{
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float apDist = std::get<0>(data);
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float rssi = std::get<2>(data);
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float rssiDist = LogDistanceModel::rssiToDistance(-40, pathLoss, rssi);
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float diff = (apDist - rssiDist);
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diffs.add(diff);
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}
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error.push_back({ pathLoss, diffs.getSquaredSumAvg() });
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}
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auto minElement = std::min_element(error.begin(), error.end(), [](std::pair<float, float> a, std::pair<float, float> b) {
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return a.second < b.second;
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});
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std::cout << "Min path loss \t" << minElement->first << "\t" << minElement->second << "\n";
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return *minElement;
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}
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void optimizeWifiParameters(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator)
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{
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int i = 1;
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for (auto nuc : { Settings::NUC1, Settings::NUC2, Settings::NUC3, Settings::NUC4 })
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{
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auto values = getFtmValues(fr, gtInterpolator, nuc);
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std::cout << "NUC" << i++ << "\n";
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optimizeFtm(values);
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optimizeRssi(values);
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}
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}
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void exportFtmValues(Offline::FileReader& fr, Interpolator<uint64_t, Point3>& gtInterpolator)
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{
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std::fstream fs;
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fs.open("test.txt", std::fstream::out);
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fs << "timestamp;nucid;dist;rssiDist;ftmDist;ftmStdDev;numMeas;numSuccesMeas" << "\n";
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for (const Offline::Entry& e : fr.getEntries())
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{
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if (e.type == Offline::Sensor::WIFI_FTM)
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{
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const Timestamp ts = Timestamp::fromMS(e.ts);
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Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0, 0, 1.3);
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auto wifi = fr.getWifiFtm()[e.idx].data;
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int nucid = Settings::data.CurrentPath.NUCs.at(wifi.getAP().getMAC()).ID;
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float ftm_offset = Settings::data.CurrentPath.NUCs.at(wifi.getAP().getMAC()).ftm_offset;
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float rssi_pathloss = Settings::data.CurrentPath.NUCs.at(wifi.getAP().getMAC()).rssi_pathloss;
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float rssiDist = LogDistanceModel::rssiToDistance(-40, rssi_pathloss, wifi.getRSSI());
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float ftmDist = wifi.getFtmDist() + ftm_offset; //in m; plus static offset
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float ftmStdDev = wifi.getFtmDistStd();
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int numMeas = wifi.getNumAttemptedMeasurements();
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int numSuccessMeas = wifi.getNumSuccessfulMeasurements();
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Point3 apPos = Settings::data.CurrentPath.NUCs.find(wifi.getAP().getMAC())->second.position;
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float apDist = gtPos.getDistance(apPos);
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fs << ts.ms() << ";" << nucid << ";" << apDist << ";" << rssiDist << ";" << ftmDist << ";" << ftmStdDev << ";" << numMeas << ";" << numSuccessMeas << "\n";
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}
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}
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fs.close();
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}
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static float kalman_procNoiseDistStdDev = 1.2f; // standard deviation of distance for process noise
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static float kalman_procNoiseVelStdDev = 0.1f; // standard deviation of velocity for process noise
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static Stats::Statistics<float> run(Settings::DataSetup setup, int walkIdx, std::string folder) {
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// reading file
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std::string currDir = std::filesystem::current_path().string();
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Floorplan::IndoorMap* map = Floorplan::Reader::readFromFile(setup.map);
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Offline::FileReader fr(setup.training[walkIdx]);
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// ground truth
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std::vector<int> gtPath = setup.gtPath;
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Interpolator<uint64_t, Point3> gtInterpolator = fr.getGroundTruthPath(map, gtPath);
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Stats::Statistics<float> errorStats;
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//calculate distance of path
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std::vector<Interpolator<uint64_t, Point3>::InterpolatorEntry> gtEntries = gtInterpolator.getEntries();
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double distance = 0;
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for(int i = 1; i < gtEntries.size(); ++i){
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distance += gtEntries[i].value.getDistance(gtEntries[i-1].value);
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}
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std::cout << "Distance of Path: " << distance << std::endl;
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// error file
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const long int t = static_cast<long int>(time(NULL));
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auto evalDir = std::experimental::filesystem::path(Settings::errorDir);
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evalDir.append(folder);
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if (!std::experimental::filesystem::exists(evalDir)) {
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std::experimental::filesystem::create_directory(evalDir);
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}
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std::ofstream errorFile;
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errorFile.open (evalDir.string() + "/" + std::to_string(walkIdx) + "_" + std::to_string(t) + ".csv");
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// wifi
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auto kalmanMap = std::make_shared<std::unordered_map<MACAddress, Kalman>>();
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kalmanMap->insert({ Settings::NUC1, Kalman(1, setup.NUCs.at(Settings::NUC1).kalman_measStdDev, kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev) });
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kalmanMap->insert({ Settings::NUC2, Kalman(2, setup.NUCs.at(Settings::NUC2).kalman_measStdDev, kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev) });
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kalmanMap->insert({ Settings::NUC3, Kalman(3, setup.NUCs.at(Settings::NUC3).kalman_measStdDev, kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev) });
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kalmanMap->insert({ Settings::NUC4, Kalman(4, setup.NUCs.at(Settings::NUC4).kalman_measStdDev, kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev) });
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std::cout << "Optimal wifi parameters for " << setup.training[walkIdx] << "\n";
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optimizeWifiParameters(fr, gtInterpolator);
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// mesh
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NM::NavMeshSettings set;
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set.maxQuality_m = 0.10; // because of narrow hallways and small rooms reduce min. triangle size (default is 0.2)
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MyNavMesh mesh;
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MyNavMeshFactory fac(&mesh, set);
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fac.build(map);
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const Point3 srcPath0(9.8, 24.9, 0); // fixed start pos
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// add shortest-path to destination
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//const Point3 dst(51, 45, 1.7);
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//const Point3 dst(25, 45, 0);
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//NM::NavMeshDijkstra::stamp<MyNavMeshTriangle>(mesh, dst);
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// debug show
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//MeshPlotter dbg;
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//dbg.addFloors(map);
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//dbg.addOutline(map);
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//dbg.addMesh(mesh);
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////dbg.addDijkstra(mesh);
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//dbg.draw();
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Plotty plot(map);
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plot.buildFloorplan();
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plot.setGroundTruth(gtPath);
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plot.setView(30, 0);
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// APs Positions
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plot.addCircle(100000 + 0, Settings::data.CurrentPath.nucInfo(0).position.xy(), 0.05);
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plot.addCircle(100000 + 1, Settings::data.CurrentPath.nucInfo(1).position.xy(), 0.05);
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plot.addCircle(100000 + 2, Settings::data.CurrentPath.nucInfo(2).position.xy(), 0.05);
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plot.addCircle(100000 + 3, Settings::data.CurrentPath.nucInfo(3).position.xy(), 0.05);
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plot.plot();
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// particle-filter
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const int numParticles = 5000;
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//auto init = std::make_unique<MyPFInitFixed>(&mesh, srcPath0); // known position
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auto init = std::make_unique<MyPFInitUniform>(&mesh); // uniform distribution
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auto eval = std::make_unique<MyPFEval>();
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eval->kalmanMap = kalmanMap;
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auto trans = std::make_unique<MyPFTrans>(mesh);
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//auto trans = std::make_unique<MyPFTransStatic>();
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auto resample = std::make_unique<SMC::ParticleFilterResamplingSimple<MyState>>();
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auto estimate = std::make_unique<SMC::ParticleFilterEstimationWeightedAverage<MyState>>();
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// setup
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MyFilter pf(numParticles, std::move(init));
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pf.setEvaluation(std::move(eval));
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pf.setTransition(std::move(trans));
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pf.setResampling(std::move(resample));
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pf.setEstimation(std::move(estimate));
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pf.setNEffThreshold(0.85);
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// sensors
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MyControl ctrl;
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MyObservation obs;
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StepDetection sd;
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PoseDetection pd;
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TurnDetection td(&pd);
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RelativePressure relBaro;
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ActivityDetector act;
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relBaro.setCalibrationTimeframe( Timestamp::fromMS(5000) );
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Timestamp lastTimestamp = Timestamp::fromMS(0);
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int i = 0;
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// parse each sensor-value within the offline data
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for (const Offline::Entry& e : fr.getEntries()) {
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const Timestamp ts = Timestamp::fromMS(e.ts);
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if (e.type == Offline::Sensor::WIFI_FTM) {
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auto ftm = fr.getWifiFtm()[e.idx].data;
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float ftm_offset = Settings::data.CurrentPath.NUCs.at(ftm.getAP().getMAC()).ftm_offset;
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float ftmDist = ftm.getFtmDist() + ftm_offset; // in m; plus static offset
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if (Settings::UseKalman)
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{
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auto& kalman = kalmanMap->at(ftm.getAP().getMAC());
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float predictDist = kalman.predict(ts, ftmDist);
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ftm.setFtmDist(predictDist);
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obs.wifi.insert_or_assign(ftm.getAP().getMAC(), ftm);
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}
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else
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{
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// MOV AVG
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if (obs.wifi.count(ftm.getAP().getMAC()) == 0)
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{
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obs.wifi.insert_or_assign(ftm.getAP().getMAC(), ftm);
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}
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else
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{
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auto currFtm = obs.wifi.find(ftm.getAP().getMAC());
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float currDist = currFtm->second.getFtmDist();
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const float alpha = 0.6;
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float newDist = alpha * currDist + (1 - alpha) * ftmDist;
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currFtm->second.setFtmDist(newDist);
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}
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}
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} else if (e.type == Offline::Sensor::WIFI) {
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//obs.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
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//ctrl.wifi = fr.getWiFiGroupedByTime()[e.idx].data;
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} else if (e.type == Offline::Sensor::ACC) {
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if (sd.add(ts, fr.getAccelerometer()[e.idx].data)) {
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++ctrl.numStepsSinceLastEval;
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}
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const Offline::TS<AccelerometerData>& _acc = fr.getAccelerometer()[e.idx];
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pd.addAccelerometer(ts, _acc.data);
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//simpleActivity walking / standing
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act.add(ts, fr.getAccelerometer()[e.idx].data);
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} else if (e.type == Offline::Sensor::GYRO) {
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const Offline::TS<GyroscopeData>& _gyr = fr.getGyroscope()[e.idx];
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const float delta_gyro = td.addGyroscope(ts, _gyr.data);
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ctrl.headingChangeSinceLastEval += delta_gyro;
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} else if (e.type == Offline::Sensor::BARO) {
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relBaro.add(ts, fr.getBarometer()[e.idx].data);
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obs.relativePressure = relBaro.getPressureRealtiveToStart();
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obs.sigmaPressure = relBaro.getSigma();
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//simpleActivity stairs up / down
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act.add(ts, fr.getBarometer()[e.idx].data);
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obs.activity = act.get();
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}
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if (ctrl.numStepsSinceLastEval > 0)
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//if (ts - lastTimestamp >= Timestamp::fromMS(500))
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//if (obs.wifi.size() == 4)
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{
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obs.currentTime = ts;
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ctrl.currentTime = ts;
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// if(ctrl.numStepsSinceLastEval > 0){
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// pf.updateTransitionOnly(&ctrl);
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// }
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MyState est = pf.update(&ctrl, obs); //pf.updateEvaluationOnly(obs);
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ctrl.afterEval();
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Point3 gtPos = gtInterpolator.get(static_cast<uint64_t>(ts.ms())) + Point3(0,0,0.1);
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lastTimestamp = ts;
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ctrl.lastEstimate = est.pos.pos;
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// draw wifi ranges
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for (auto& ftm : obs.wifi)
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{
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int nucid = Settings::data.CurrentPath.NUCs.at(ftm.second.getAP().getMAC()).ID;
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if (nucid == 1)
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{
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Point3 apPos = Settings::data.CurrentPath.NUCs.find(ftm.first)->second.position;
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//plot.addCircle(nucid, apPos.xy(), ftm.second.getFtmDist());
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}
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}
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obs.wifi.clear();
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//plot
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//dbg.showParticles(pf.getParticles());
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//dbg.setCurPos(est.pos.pos);
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//dbg.setGT(gtPos);
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//dbg.addEstimationNode(est.pos.pos);
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//dbg.addGroundTruthNode(gtPos);
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//dbg.setTimeInMinute(static_cast<int>(ts.sec()) / 60, static_cast<int>(static_cast<int>(ts.sec())%60));
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//dbg.draw();
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//plot.printOverview("test");
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plot.showParticles(pf.getParticles());
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plot.setCurEst(est.pos.pos);
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plot.setGroundTruth(gtPos);
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plot.addEstimationNode(est.pos.pos);
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plot.setActivity((int) act.get());
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//plot.splot.getView().setEnabled(false);
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//plot.splot.getView().setCamera(0, 0);
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//plot.splot.getView().setEqualXY(true);
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// plot.plot();
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plot.plot();
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//plot.closeStream();
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std::this_thread::sleep_for(100ms);
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// error calc
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// float err_m = gtPos.getDistance(est.pos.pos);
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// errorStats.add(err_m);
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// errorFile << ts.ms() << " " << err_m << "\n";
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//error calc with penalty for wrong floor
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double errorFactor = 3.0;
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Point3 gtPosError = Point3(gtPos.x, gtPos.y, errorFactor * gtPos.z);
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Point3 estError = Point3(est.pos.pos.x, est.pos.pos.y, errorFactor * est.pos.pos.z);
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float err_m = gtPosError.getDistance(estError);
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errorStats.add(err_m);
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errorFile << ts.ms() << " " << err_m << "\n";
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}
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}
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// get someting on console
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std::cout << "Statistical Analysis Filtering: " << std::endl;
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std::cout << "Median: " << errorStats.getMedian() << " Average: " << errorStats.getAvg() << " Std: " << errorStats.getStdDev() << std::endl;
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// save the statistical data in file
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errorFile << "========================================================== \n";
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errorFile << "Average of all statistical data: \n";
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errorFile << "Median: " << errorStats.getMedian() << "\n";
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errorFile << "Average: " << errorStats.getAvg() << "\n";
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errorFile << "Standard Deviation: " << errorStats.getStdDev() << "\n";
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errorFile << "75 Quantil: " << errorStats.getQuantile(0.75) << "\n";
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errorFile.close();
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return errorStats;
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}
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int main(int argc, char** argv)
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{
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|
CmdArguments args(argc, argv);
|
|
|
|
if (args.hasFlag("prob"))
|
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{
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|
std::cout << "Probabilistic" << "\n";
|
|
return mainProp(argc, argv);
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|
}
|
|
else if (args.hasFlag("trilat"))
|
|
{
|
|
std::cout << "Trilateration" << "\n";
|
|
return mainTrilat(argc, argv);
|
|
}
|
|
|
|
//mainFtm(argc, argv);
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//return 0;
|
|
|
|
Stats::Statistics<float> statsAVG;
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|
Stats::Statistics<float> statsMedian;
|
|
Stats::Statistics<float> statsSTD;
|
|
Stats::Statistics<float> statsQuantil;
|
|
Stats::Statistics<float> tmp;
|
|
|
|
std::string evaluationName = "prologic/tmp";
|
|
|
|
std::vector<std::array<float, 3>> error;
|
|
std::ofstream error_out;
|
|
error_out.open(Settings::errorDir + evaluationName + "_error_path1" + ".csv", std::ios_base::app);
|
|
|
|
|
|
//for (kalman_procNoiseDistStdDev = 0.8f; kalman_procNoiseDistStdDev < 1.5f; kalman_procNoiseDistStdDev += 0.1f)
|
|
//{
|
|
// for (kalman_procNoiseVelStdDev = 0.1f; kalman_procNoiseVelStdDev < 0.5f; kalman_procNoiseVelStdDev += 0.1f)
|
|
// {
|
|
|
|
for (size_t walkIdx = 0; walkIdx < 6; walkIdx++)
|
|
{
|
|
std::cout << "Executing walk " << walkIdx << "\n";
|
|
for (int i = 0; i < 1; ++i)
|
|
{
|
|
std::cout << "Start of iteration " << i << "\n";
|
|
|
|
tmp = run(Settings::data.CurrentPath, walkIdx, evaluationName);
|
|
statsMedian.add(tmp.getMedian());
|
|
statsAVG.add(tmp.getAvg());
|
|
statsSTD.add(tmp.getStdDev());
|
|
statsQuantil.add(tmp.getQuantile(0.75));
|
|
|
|
std::cout << kalman_procNoiseDistStdDev << " " << kalman_procNoiseVelStdDev << std::endl;
|
|
std::cout << "Iteration " << i << " completed" << std::endl;
|
|
|
|
}
|
|
}
|
|
|
|
|
|
// error.push_back({{ kalman_procNoiseDistStdDev, kalman_procNoiseVelStdDev, statsAVG.getAvg() }});
|
|
|
|
// auto minElement = std::min_element(error.begin(), error.end(), [](std::array<float, 3> a, std::array<float, 3> b) {
|
|
// return a[2] < b[2];
|
|
// });
|
|
|
|
// std::cout << "Current min error " << (*minElement)[2] << "\t Q(0)=\t" << (*minElement)[0] << "\t Q(1)=" << (*minElement)[1] << "\n";
|
|
|
|
// error_out << kalman_procNoiseDistStdDev << ";" << kalman_procNoiseVelStdDev << ";" << statsAVG.getAvg() << std::endl;
|
|
|
|
// // reset stats
|
|
// statsAVG.reset();
|
|
// statsMedian.reset();
|
|
// statsSTD.reset();
|
|
// statsQuantil.reset();
|
|
// }
|
|
//}
|
|
|
|
//auto minElement = std::min_element(error.begin(), error.end(), [](std::array<float, 3> a, std::array<float, 3> b) {
|
|
// return a[2] < b[2];
|
|
//});
|
|
|
|
//std::cout << "Global Min error " << (*minElement)[2] << "\t Q(0)=\t" << (*minElement)[0] << "\t Q(1)=" << (*minElement)[1] << "\n";
|
|
|
|
//error_out.close();
|
|
|
|
|
|
//for(int i = 0; i < 2; ++i){
|
|
//
|
|
// tmp = run(Settings::data.CurrentPath, 0, evaluationName);
|
|
// statsMedian.add(tmp.getMedian());
|
|
// statsAVG.add(tmp.getAvg());
|
|
// statsSTD.add(tmp.getStdDev());
|
|
// statsQuantil.add(tmp.getQuantile(0.75));
|
|
|
|
// std::cout << "Iteration " << i << " completed" << std::endl;
|
|
//}
|
|
|
|
std::cout << "==========================================================" << std::endl;
|
|
std::cout << "Average of all statistical data: " << std::endl;
|
|
std::cout << "Median: " << statsMedian.getAvg() << std::endl;
|
|
std::cout << "Average: " << statsAVG.getAvg() << std::endl;
|
|
std::cout << "Standard Deviation: " << statsSTD.getAvg() << std::endl;
|
|
std::cout << "75 Quantil: " << statsQuantil.getAvg() << std::endl;
|
|
std::cout << "==========================================================" << std::endl;
|
|
|
|
//EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS EDIT THIS
|
|
std::ofstream finalStatisticFile;
|
|
finalStatisticFile.open (Settings::errorDir + evaluationName + ".csv", std::ios_base::app);
|
|
|
|
finalStatisticFile << "========================================================== \n";
|
|
finalStatisticFile << "Average of all statistical data: \n";
|
|
finalStatisticFile << "Median: " << statsMedian.getAvg() << "\n";
|
|
finalStatisticFile << "Average: " << statsAVG.getAvg() << "\n";
|
|
finalStatisticFile << "Standard Deviation: " << statsSTD.getAvg() << "\n";
|
|
finalStatisticFile << "75 Quantil: " << statsQuantil.getAvg() << "\n";
|
|
finalStatisticFile << "========================================================== \n";
|
|
|
|
finalStatisticFile.close();
|
|
|
|
//std::this_thread::sleep_for(std::chrono::seconds(60));
|
|
|
|
}
|