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nParticleFilter.cpp
148 lines (122 loc) · 3.63 KB
/
nParticleFilter.cpp
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#include "nParticleFilter.hpp"
namespace nP{
const int ParticleFilter::particleCount = 300;
const double ParticleFilter::xMin = 0.0;
const double ParticleFilter::xMax = 400.0;
const double ParticleFilter::yMin = 0.0;
const double ParticleFilter::yMax = 400.0;
const double ParticleFilter::dMin = 0.0;
const double ParticleFilter::dMax = 2.0 * M_PI;
ParticleFilter::ParticleFilter() : particles(particleCount){
std::random_device rd;
rnd = std::mt19937(rd()); // Seed
randomizeParticles();
}
void ParticleFilter::randomizeParticles(){
std::uniform_real_distribution<> xDist(xMin, xMax);
std::uniform_real_distribution<> yDist(yMin, yMax);
std::uniform_real_distribution<> dDist(dMin, dMax);
for(int i = 0; i < particleCount; i++){
particles[i].x = xDist(rnd);
particles[i].y = yDist(rnd);
particles[i].d = dDist(rnd);
particles[i].w = 0.0;
}
}
std::vector<Particle>& ParticleFilter::getParticles(){
return particles;
}
void ParticleFilter::filter(double camX, double camY, double camD, double* resX, double* resY, double* resD){
resample();
predict();
weight(camX, camY, camD);
measure(resX, resY, resD);
}
void ParticleFilter::measure(double* x, double* y, double* d){
// Return weighted-Avg
double dx = 0.0;
double dy = 0.0;
double sumX = 0.0;
double sumY = 0.0;
for(int i = 0; i < particleCount; i++){
sumX += particles[i].x * particles[i].w;
sumY += particles[i].y * particles[i].w;
dx += cos(particles[i].d) * particles[i].w;
dy += sin(particles[i].d) * particles[i].w;
}
*x = sumX;
*y = sumY;
*d = atan2(dy, dx);
lastX = *x;
lastY = *y;
lastD = *d;
}
void ParticleFilter::weight(double x, double y, double d){
// Calculate likelihood
double sumWeight = 0.0;
for(int i = 0; i < particleCount; i++){
double dx = (x - particles[i].x) / 20.0;
double dy = (y - particles[i].y) / 20.0;
double dd = dirDiff(d, particles[i].d);
particles[i].w = exp(-(dx*dx + dy*dy + dd*dd)) + 0.00001;
sumWeight += particles[i].w;
}
// Normalize weights
for(int i = 0; i < particleCount; i++){
particles[i].w /= sumWeight;
}
}
void ParticleFilter::predict(){
// Noise, Linear motion ... etc
std::normal_distribution<> xyDist(0.0, 10.0);
std::normal_distribution<> dDist(0.0, 10.0*M_PI/180.0);
for(int i = 0; i < particleCount; i++){
particles[i].x += xyDist(rnd);
particles[i].y += xyDist(rnd);
particles[i].d += dDist(rnd);
particles[i].x += cos(lastD);
particles[i].y += sin(lastD);
if(particles[i].x < xMin)particles[i].x = xMin;
if(xMax < particles[i].x)particles[i].x = xMax;
if(particles[i].y < yMin)particles[i].y = yMin;
if(yMax < particles[i].y)particles[i].y = yMax;
if(particles[i].d < 0.0)particles[i].d += 2.0*M_PI;
if(2.0*M_PI < particles[i].d)particles[i].d -= 2.0*M_PI;
}
}
void ParticleFilter::resample(){
double accumW[particleCount];
accumW[0] = {particles[0].w};
for(int i = 1; i < particleCount; i++){
accumW[i] = accumW[i-1] + particles[i].w;
}
// Copy
std::vector<Particle> tmp;
for(int i = 0; i < particleCount; i++){
tmp.push_back(particles[i]);
}
// Selection
std::uniform_real_distribution<> dist(0.0, 1.0);
for(int i = 0; i < particleCount; i++){
double d = dist(rnd);
for(int j = 0; j < particleCount; j++){
if(d > accumW[j])continue;
particles[i].x = tmp[j].x;
particles[i].y = tmp[j].y;
particles[i].d = tmp[j].d;
particles[i].w = 0.0;
break;
}
}
}
double dirDiff(double dir1, double dir2){
double tmp = dir2 - dir1;
while(tmp < -M_PI){
tmp += 2.0 * M_PI;
}
while(M_PI < tmp){
tmp -= 2.0 * M_PI;
}
return tmp;
}
};