ParticleFilter.h

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/*
Copyright (c) 2010-2016, Mathieu Labbe - IntRoLab - Universite de Sherbrooke
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
    * Redistributions of source code must retain the above copyright
      notice, this list of conditions and the following disclaimer.
    * Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in the
      documentation and/or other materials provided with the distribution.
    * Neither the name of the Universite de Sherbrooke nor the
      names of its contributors may be used to endorse or promote products
      derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
(INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/

#ifndef PARTICLEFILTER_H_
#define PARTICLEFILTER_H_

#include <rtabmap/utilite/UMath.h>
#include <rtabmap/utilite/ULogger.h>


namespace rtabmap {

// taken from http://www.developpez.net/forums/d544518/c-cpp/c/equivalent-randn-matlab-c/
#define TWOPI (6.2831853071795864769252867665590057683943387987502) /* 2 * pi */

/*
   RAND is a macro which returns a pseudo-random numbers from a uniform
   distribution on the interval [0 1]
*/
#define RAND (rand())/((double) RAND_MAX)

/*
   RANDN is a macro which returns a pseudo-random numbers from a normal
   distribution with mean zero and standard deviation one. This macro uses Box
   Muller's algorithm
*/
#define RANDN (sqrt(-2.0*log(RAND))*cos(TWOPI*RAND))

std::vector<double> cumSum(const std::vector<double> & v)
{
        std::vector<double> cum(v.size());
        double sum = 0;
        for(unsigned int i=0; i<v.size(); ++i)
        {
                cum[i] = v[i] + sum;
                sum += v[i];
        }
        return cum;
}

std::vector<double> resample(const std::vector<double> & p, // particles
                                                         const std::vector<double> & w, // weights
                                                         bool normalizeWeights = false)
{
        std::vector<double> np; //new particles
        if(p.size() != w.size() || p.size() == 0)
        {
                UERROR("particles (%d) and weights (%d) are not the same size", p.size(), w.size());
                return np;
        }

        std::vector<double> cs;
        if(normalizeWeights)
        {
                double wSum = uSum(w);
                std::vector<double> wNorm(w.size());
                for(unsigned int i=0; i<w.size(); ++i)
                {
                        wNorm[i] = w[i]/wSum;
                }
                cs = cumSum(wNorm); // cumulative sum
        }
        else
        {
                cs = cumSum(w); // cumulative sum
        }
        for(unsigned int j=0; j<cs.size(); ++j)
        {
                cs[j]/=cs.back();
        }

        np.resize(p.size());
        for(unsigned int i=0; i<np.size(); ++i)
        {
                unsigned int index = 0;
                double randnum = RAND;
                for(unsigned int j=0; j<cs.size(); ++j)
                {
                        if(randnum < cs[j])
                        {
                                index = j;
                                break;
                        }
                }
                np[i] = p[index];
        }
        return np;
}


class ParticleFilter
{
public:
        ParticleFilter(unsigned int nParticles = 200,
                                   double noise = 0.1,
                                   double lambda = 10.0,
                                   double initValue = 0.0) :
                noise_(noise),
                lambda_(lambda)
        {
                particles_.resize(nParticles, initValue);
        }

        void init(double initValue = 0.0f)
        {
                particles_ = std::vector<double>(particles_.size(), initValue);
        }

        double filter(double val)
        {
                std::vector<double> weights(particles_.size(), 1);
                double sumWeights = 0;
                for(unsigned int i=0; i<particles_.size(); ++i)
                {
                        // add noise to particle
                        particles_[i] += noise_ * RANDN;

                        // compute weight
                        double dist = fabs(particles_[i] - val);
                        //dist = sqrt(dist*dist);
                        double w = exp(-lambda_*dist);
                        if(uIsFinite(w) && w > 0)
                        {
                                weights[i] = w;
                        }
                        sumWeights += weights[i];
                }


                //normalize and compute estimated value
                double value =0.0;
                for(unsigned int i=0; i<weights.size(); ++i)
                {
                        weights[i] /= sumWeights;
                        value += weights[i] * particles_[i];
                }

                //resample the particles
                particles_ = resample(particles_, weights, false);

                return value;
        }

private:
        std::vector<double> particles_;
        double noise_;
        double lambda_;
};

}


#endif /* PARTICLEFILTER_H_ */