144 lines
5.1 KiB
C++
144 lines
5.1 KiB
C++
#ifndef MYSMOOTHINGTRANSITION_H
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#define MYSMOOTHINGTRANSITION_H
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#include <KLib/math/filter/particles/ParticleFilterTransition.h>
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#include <KLib/math/filter/smoothing/BackwardFilterTransition.h>
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#include <KLib/math/distribution/Normal.h>
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#include <KLib/math/distribution/Uniform.h>
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#include <Indoor/nav/dijkstra/Dijkstra.h>
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#include <Indoor/grid/Grid.h>
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#include "../MyState.h"
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#include "../MyControl.h"
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#include "../../DijkstraMapper.h"
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#include "../../toni/barometric.h"
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#include <map>
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static double smoothing_walk_mu = 0.7;
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static double smoothing_walk_sigma = 0.5;
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static double smoothing_heading_sigma = 15.0;
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static double smoothing_baro_sigma = 0.2;
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class MySmoothingTransition : public K::BackwardFilterTransition<MyState> {
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private:
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/** the created grid to draw transitions from */
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Grid<MyGridNode>* grid;
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/** a simple normal distribution */
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K::NormalDistribution distWalk;
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public:
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/**
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* ctor
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* @param choice the choice to use for randomly drawing nodes
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* @param fp the underlying floorplan
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*/
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MySmoothingTransition(Grid<MyGridNode>* grid) :
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grid(grid), distWalk(smoothing_walk_mu, smoothing_walk_sigma) {
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distWalk.setSeed(4321);
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}
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public:
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uint64_t ts = 0;
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uint64_t deltaMS = 0;
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/** set the current time in millisconds */
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void setCurrentTime(const uint64_t ts) {
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if (this->ts == 0) {
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this->ts = ts;
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deltaMS = 0;
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} else {
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deltaMS = this->ts - ts;
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this->ts = ts;
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}
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}
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/**
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* smoothing transition starting at T with t, t-1,...0
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* @param particles_new p_t (Forward Filter)
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* @param particles_old p_t+1 (Smoothed Particles from Step before)
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*/
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std::vector<std::vector<double>> transition(std::vector<K::Particle<MyState>>const& particles_new,
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std::vector<K::Particle<MyState>>const& particles_old ) override {
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// calculate alpha(m,n) = p(q_t+1(m) | q_t(n))
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// this means, predict all possible states q_t+1 with all passible states q_t
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// e.g. p(q_490(1)|q_489(1));p(q_490(1)|q_489(2)) ... p(q_490(1)|q_489(N)) and
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// p(q_490(1)|q_489(1)); p(q_490(2)|q_489(1)) ... p(q_490(M)|q_489(1))
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std::vector<std::vector<double>> predictionProbabilities;
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auto p1 = particles_old.begin();
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auto p2 = particles_new.begin();
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#pragma omp parallel for private(p2) shared(predictionProbabilities)
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for (p1 = particles_old.begin(); p1 < particles_old.end(); ++p1) {
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std::vector<double> innerVector;
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for(p2 = particles_new.begin(); p2 < particles_new.end(); ++p2){
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// find the node (square) the particle is within
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// just to be safe, we round z to the nearest floor
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//TODO:: Nullptr check! sometimes src/dst can be nullptr
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//const Node3* dst = graph->getNearestNode(p1->state.x_cm, p1->state.y_cm, std::round(p1->state.z_nr));
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//const Node3* src = graph->getNearestNode(p2->state.x_cm, p2->state.y_cm, std::round(p2->state.z_nr));
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const MyGridNode* dst = grid->getNodePtrFor(GridPoint(p1->state.pCur.x, p1->state.pCur.y, p1->state.pCur.z));
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const MyGridNode* src = grid->getNodePtrFor(GridPoint(p2->state.pCur.x, p2->state.pCur.y, p2->state.pCur.z));
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Dijkstra<MyGridNode> dijkstra;
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dijkstra.build(src, dst, DijkstraMapper(*grid));
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double distDijkstra_m = dijkstra.getNode(*src)->cumWeight;
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const double distProb = distWalk.getProbability(distDijkstra_m);
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//getProb using the angle(heading) between src and dst
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double angle = 0.0;
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if(!(p2->state.pCur.x == p1->state.pCur.x) && !(p2->state.pCur.y == p1->state.pCur.y)){
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angle = Angle::getDEG_360(p2->state.pCur.x, p2->state.pCur.y, p1->state.pCur.x, p1->state.pCur.y);
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}
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const double headingProb = K::NormalDistribution::getProbability(p1->state.cumulativeHeading, smoothing_heading_sigma, angle);
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//assert(headingProb != 0.0);
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//assert(distProb != 0.0);
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//check how near we are to the measurement
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double floorProb = K::NormalDistribution::getProbability(p1->state.measurement_pressure, smoothing_baro_sigma, p2->state.hPa);
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//combine the probabilities
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double prob = distProb * floorProb * headingProb;
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innerVector.push_back(prob);
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//if(distance_m != distance_m) {throw "detected NaN";}
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//if(distProb != distProb) {throw "detected NaN";}
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if(angle != angle) {throw "detected NaN";}
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if(headingProb != headingProb) {throw "detected NaN";}
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if(floorProb != floorProb) {throw "detected NaN";}
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if(floorProb == 0) {throw "detected NaN";}
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if(prob != prob) {throw "detected NaN";}
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//assert(prob != 0.0);
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}
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#pragma omp critical
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predictionProbabilities.push_back(innerVector);
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}
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return predictionProbabilities;
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}
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};
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#endif // MYTRANSITION_H
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