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/* +---------------------------------------------------------------------------+
   |          The Mobile Robot Programming Toolkit (MRPT) C++ library          |
   |                                                                           |
   |                   http://mrpt.sourceforge.net/                            |
   |                                                                           |
   |   Copyright (C) 2005-2010  University of Malaga                           |
   |                                                                           |
   |    This software was written by the Machine Perception and Intelligent    |
   |      Robotics Lab, University of Malaga (Spain).                          |
   |    Contact: Jose-Luis Blanco  <jlblanco@ctima.uma.es>                     |
   |                                                                           |
   |  This file is part of the MRPT project.                                   |
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   |     MRPT is free software: you can redistribute it and/or modify          |
   |     it under the terms of the GNU General Public License as published by  |
   |     the Free Software Foundation, either version 3 of the License, or     |
   |     (at your option) any later version.                                   |
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   |   MRPT is distributed in the hope that it will be useful,                 |
   |     but WITHOUT ANY WARRANTY; without even the implied warranty of        |
   |     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         |
   |     GNU General Public License for more details.                          |
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   |     You should have received a copy of the GNU General Public License     |
   |     along with MRPT.  If not, see <http://www.gnu.org/licenses/>.         |
   |                                                                           |
   +---------------------------------------------------------------------------+ */

#ifndef PF_implementations_H
#define PF_implementations_H

#include <mrpt/utils/stl_extensions.h>
#include <mrpt/bayes/CParticleFilterData.h>
#include <mrpt/random.h>
#include <mrpt/slam/CActionCollection.h>
#include <mrpt/slam/CActionRobotMovement3D.h>
#include <mrpt/slam/CActionRobotMovement2D.h>
#include <mrpt/slam/TKLDParams.h>

#include <mrpt/math/distributions.h>  // chi2inv

#include <mrpt/slam/PF_implementations_data.h>

#include <mrpt/slam/link_pragmas.h>


/** \file PF_implementations.h
  *  This file contains the implementations of the template members declared in mrpt::slam::PF_implementation
  */

namespace mrpt
{
        namespace slam
        {
                using namespace std;
                using namespace mrpt::utils;
                using namespace mrpt::random;
                using namespace mrpt::poses;
                using namespace mrpt::bayes;
                using namespace mrpt::math;

                /** Auxiliary method called by PF implementations: return true if we have both action & observation,
                  *   otherwise, return false AND accumulate the odometry so when we have an observation we didn't lose a thing.
                  *   On return=true, the "m_movementDrawer" member is loaded and ready to draw samples of the increment of pose since last step.
                  *  This method is smart enough to accumulate CActionRobotMovement2D or CActionRobotMovement3D, whatever comes in.
                  */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                bool PF_implementation<PARTICLE_TYPE>::PF_SLAM_implementation_gatherActionsCheckBothActObs(
                        const CActionCollection * actions,
                        const CSensoryFrame             * sf )
                {
                        if (actions!=NULL)      // A valid action?
                        {
                                {
                                        CActionRobotMovement2DPtr       robotMovement2D = actions->getBestMovementEstimation();
                                        if (robotMovement2D.present())
                                        {
                                                if (m_accumRobotMovement3DIsValid) THROW_EXCEPTION("Mixing 2D and 3D actions is not allowed.")

                                                if (!m_accumRobotMovement2DIsValid)
                                                {               // First time:
                                                        robotMovement2D->poseChange->getMean( m_accumRobotMovement2D.rawOdometryIncrementReading );
                                                        m_accumRobotMovement2D.motionModelConfiguration = robotMovement2D->motionModelConfiguration;
                                                }
                                                else  m_accumRobotMovement2D.rawOdometryIncrementReading += robotMovement2D->poseChange->getMeanVal();

                                                m_accumRobotMovement2DIsValid = true;
                                        }
                                        else // If there is no 2D action, look for a 3D action:
                                        {
                                                CActionRobotMovement3DPtr       robotMovement3D = actions->getActionByClass<CActionRobotMovement3D>();
                                                if (robotMovement3D)
                                                {
                                                        if (m_accumRobotMovement2DIsValid) THROW_EXCEPTION("Mixing 2D and 3D actions is not allowed.")

                                                        if (!m_accumRobotMovement3DIsValid)
                                                                        m_accumRobotMovement3D = robotMovement3D->poseChange;
                                                        else    m_accumRobotMovement3D += robotMovement3D->poseChange;
                                                        // This "+=" takes care of all the Jacobians, etc... You MUST love C++!!! ;-)

                                                        m_accumRobotMovement3DIsValid = true;
                                                }
                                                else
                                                        return false; // We have no actions...
                                        }
                                }
                        }

                        const bool SFhasValidObservations = (sf==NULL) ? false : PF_SLAM_implementation_doWeHaveValidObservations(m_partdata.m_particles,sf);

                        // All the things we need?
                        if (! ((m_accumRobotMovement2DIsValid || m_accumRobotMovement3DIsValid) && SFhasValidObservations))
                                return false;

                        // Since we're gonna return true, load the pose-drawer:
                        // Take the accum. actions as input:
                        if (m_accumRobotMovement3DIsValid)
                        {
                                m_movementDrawer.setPosePDF( m_accumRobotMovement3D );  // <--- Set mov. drawer
                                m_accumRobotMovement3DIsValid = false; // Reset odometry for next iteration
                        }
                        else
                        {
                                CActionRobotMovement2D  theResultingRobotMov;
                                theResultingRobotMov.computeFromOdometry(
                                        m_accumRobotMovement2D.rawOdometryIncrementReading,
                                        m_accumRobotMovement2D.motionModelConfiguration );

                                m_movementDrawer.setPosePDF( theResultingRobotMov.poseChange );  // <--- Set mov. drawer
                                m_accumRobotMovement2DIsValid = false; // Reset odometry for next iteration
                        }
                        return true;
                } // end of PF_SLAM_implementation_gatherActionsCheckBothActObs

                /** A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFOptimal" (optimal sampling with rejection sampling approximation),
                  *  common to both localization and mapping.
                  *
                  * - BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.
                  *
                  *  This method implements optimal sampling with a rejection sampling-based approximation of the true posterior.
                  *  For details, see the papers:
                  *
                  *  J.-L. Blanco, J. González, and J.-A. Fernández-Madrigal,
                  *    "An Optimal Filtering Algorithm for Non-Parametric Observation Models in
                  *     Robot Localization," in Proc. IEEE International Conference on Robotics
                  *     and Automation (ICRA'08), 2008, pp. 461–466.
                  */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                void PF_implementation<PARTICLE_TYPE>::PF_SLAM_implementation_pfAuxiliaryPFOptimal(
                        const CActionCollection * actions,
                        const CSensoryFrame             * sf,
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const TKLDParams &KLD_options)
                {
                        // Standard and Optimal AuxiliaryPF actually have a shared implementation body:
                        PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal<BINTYPE>(actions,sf,PF_options,KLD_options, true /*Optimal PF*/ );
                }


                /** A generic implementation of the PF method "pfStandardProposal" (standard proposal distribution, that is, a simple SIS particle filter),
                  *  common to both localization and mapping.
                  *
                  * - BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.
                  */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                void PF_implementation<PARTICLE_TYPE>::PF_SLAM_implementation_pfStandardProposal(
                        const CActionCollection * actions,
                        const CSensoryFrame             * sf,
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const TKLDParams &KLD_options)
                {
                        MRPT_START
                        typedef std::set<BINTYPE,typename BINTYPE::lt_operator>         TSetStateSpaceBins;

                        // In this method we don't need the "PF_SLAM_implementation_gatherActionsCheckBothActObs" machinery,
                        //  since prediction & update are two independent stages well separated for this algorithm.

                        // --------------------------------------------------------------------------------------
                        //  Prediction: Simply draw samples from the motion model
                        // --------------------------------------------------------------------------------------
                        if (actions)
                        {
                                // Find a robot movement estimation:
                                CPose3D                         motionModelMeanIncr;
                                {
                                        CActionRobotMovement2DPtr       robotMovement2D = actions->getBestMovementEstimation();
                                        // If there is no 2D action, look for a 3D action:
                                        if (robotMovement2D.present())
                                        {
                                                m_movementDrawer.setPosePDF( robotMovement2D->poseChange );
                                                motionModelMeanIncr = robotMovement2D->poseChange->getMeanVal();
                                        }
                                        else
                                        {
                                                CActionRobotMovement3DPtr       robotMovement3D = actions->getActionByClass<CActionRobotMovement3D>();
                                                if (robotMovement3D)
                                                {
                                                        m_movementDrawer.setPosePDF( robotMovement3D->poseChange );
                                                        robotMovement3D->poseChange.getMean( motionModelMeanIncr );
                                                }
                                                else { THROW_EXCEPTION("Action list does not contain any CActionRobotMovement2D or CActionRobotMovement3D object!"); }
                                        }
                                }

                                // Update particle poses:
                                if ( !PF_options.adaptiveSampleSize )
                                {
                                        const size_t M = m_partdata.m_particles.size();
                                        // -------------------------------------------------------------
                                        // FIXED SAMPLE SIZE
                                        // -------------------------------------------------------------
                                        CPose3D incrPose;
                                        for (size_t i=0;i<M;i++)
                                        {
                                                // Generate gaussian-distributed 2D-pose increments according to mean-cov:
                                                m_movementDrawer.drawSample( incrPose );
                                                CPose3D finalPose = CPose3D(*getLastPose(i)) + incrPose;

                                                // Update the particle with the new pose: this part is caller-dependant and must be implemented there:
                                                PF_SLAM_implementation_custom_update_particle_with_new_pose( m_partdata.m_particles[i].d, TPose3D(finalPose) );
                                        }
                                }
                                else
                                {
                                        // -------------------------------------------------------------
                                        //   ADAPTIVE SAMPLE SIZE
                                        // Implementation of Dieter Fox's KLD algorithm
                                        //  31-Oct-2006 (JLBC): First version
                                        //  19-Jan-2009 (JLBC): Rewriten within a generic template
                                        // -------------------------------------------------------------
                                        TSetStateSpaceBins stateSpaceBins;

                                        size_t Nx = KLD_options.KLD_minSampleSize;
                                        const double delta_1 = 1.0 - KLD_options.KLD_delta;
                                        const double epsilon_1 = 0.5 / KLD_options.KLD_epsilon;

                                        // Prepare data for executing "fastDrawSample"
                                        m_pfc.prepareFastDrawSample(PF_options);

                                        // The new particle set:
                                        std::vector<TPose3D>    newParticles;
                                        vector_double                   newParticlesWeight;
                                        vector<size_t>                  newParticlesDerivedFromIdx;

                                        CPose3D  increment_i;
                                        size_t N = 1;

                                        do      // THE MAIN DRAW SAMPLING LOOP
                                        {
                                                // Draw a robot movement increment:
                                                m_movementDrawer.drawSample( increment_i );

                                                // generate the new particle:
                                                const size_t drawn_idx = m_pfc.fastDrawSample(PF_options);
                                                const CPose3D newPose = CPose3D(*getLastPose(drawn_idx)) + increment_i;
                                                const TPose3D newPose_s = newPose;

                                                // Add to the new particles list:
                                                newParticles.push_back( newPose_s );
                                                newParticlesWeight.push_back(0);
                                                newParticlesDerivedFromIdx.push_back(drawn_idx);

                                                // Now, look if the particle falls in a new bin or not:
                                                // --------------------------------------------------------
                                                BINTYPE p;
                                                KLF_loadBinFromParticle<PARTICLE_TYPE,BINTYPE>(p,KLD_options, m_partdata.m_particles[drawn_idx].d, &newPose_s);

                                                if (stateSpaceBins.find( p )==stateSpaceBins.end())
                                                {
                                                        // It falls into a new bin:
                                                        // Add to the stateSpaceBins:
                                                        stateSpaceBins.insert( p );

                                                        // K = K + 1
                                                        size_t  K = stateSpaceBins.size();
                                                        if ( K>1) //&& newParticles.size() > options.KLD_minSampleSize )
                                                        {
                                                                // Update the number of m_particles!!
                                                                Nx =  round(epsilon_1 * math::chi2inv(delta_1,K-1));
                                                                //printf("k=%u \tn=%u \tNx:%u\n", k, newParticles.size(), Nx);
                                                        }
                                                }
                                                N = newParticles.size();
                                        } while (       N < max(Nx,(size_t)KLD_options.KLD_minSampleSize) &&
                                                                N < KLD_options.KLD_maxSampleSize );

                                        // ---------------------------------------------------------------------------------
                                        // Substitute old by new particle set:
                                        //   Old are in "m_particles"
                                        //   New are in "newParticles", "newParticlesWeight","newParticlesDerivedFromIdx"
                                        // ---------------------------------------------------------------------------------
                                        this->PF_SLAM_implementation_replaceByNewParticleSet(
                                                m_partdata.m_particles,
                                                newParticles,newParticlesWeight,newParticlesDerivedFromIdx );

                                } // end adaptive sample size
                        }

                        if (sf)
                        {
                                const size_t M = m_partdata.m_particles.size();
                                //      UPDATE STAGE
                                // ----------------------------------------------------------------------
                                // Compute all the likelihood values & update particles weight:
                                for (size_t i=0;i<M;i++)
                                {
                                        const TPose3D  *partPose= getLastPose(i); // Take the particle data:
                                        CPose3D  partPose2 = CPose3D(*partPose);
                                        const double obs_log_likelihood = PF_SLAM_computeObservationLikelihoodForParticle(PF_options,i,*sf,partPose2);
                                        m_partdata.m_particles[i].log_w += obs_log_likelihood * PF_options.powFactor;
                                } // for each particle "i"

                                // Normalization of weights is done outside of this method automatically.
                        }

                        MRPT_END
                }  // end of PF_SLAM_implementation_pfStandardProposal

                /** A generic implementation of the PF method "prediction_and_update_pfAuxiliaryPFStandard" (Auxiliary particle filter with the standard proposal),
                  *  common to both localization and mapping.
                  *
                  * - BINTYPE: TPoseBin or whatever to discretize the sample space for KLD-sampling.
                  *
                  *  This method is described in the paper:
                  *   Pitt, M.K.; Shephard, N. (1999). "Filtering Via Simulation: Auxiliary Particle Filters".
                  *    Journal of the American Statistical Association 94 (446): 590–591. doi:10.2307/2670179.
                  *
                  */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                void PF_implementation<PARTICLE_TYPE>::PF_SLAM_implementation_pfAuxiliaryPFStandard(
                        const CActionCollection * actions,
                        const CSensoryFrame             * sf,
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const TKLDParams &KLD_options)
                {
                        // Standard and Optimal AuxiliaryPF actually have a shared implementation body:
                        PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal<BINTYPE>(actions,sf,PF_options,KLD_options, false /*APF*/ );
                }

                /*---------------------------------------------------------------
                                        PF_SLAM_particlesEvaluator_AuxPFOptimal
                 ---------------------------------------------------------------*/
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                double  PF_implementation<PARTICLE_TYPE>::PF_SLAM_particlesEvaluator_AuxPFOptimal(
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const CParticleFilterCapable    *obj,
                        size_t                                  index,
                        const void                              *action,
                        const void                              *observation )
                {
                        MRPT_START

                        const PF_implementation<PARTICLE_TYPE> *myObj = reinterpret_cast<const PF_implementation<PARTICLE_TYPE>*>( obj );

                        // Compute the quantity:
                        //     w[i]·p(zt|z^{t-1},x^{[i],t-1})
                        // As the Monte-Carlo approximation of the integral over all posible $x_t$.
                        // --------------------------------------------
                        double  indivLik, maxLik= -1e300;
                        CPose3D maxLikDraw;
                        size_t  N = PF_options.pfAuxFilterOptimal_MaximumSearchSamples;
                        ASSERT_(N>1)

                        const CPose3D oldPose = *myObj->getLastPose(index);
                        vector_double   vectLiks(N,0);          // The vector with the individual log-likelihoods.
                        CPose3D                 drawnSample;
                        for (size_t q=0;q<N;q++)
                        {
                                myObj->m_movementDrawer.drawSample(drawnSample);
                                CPose3D x_predict = oldPose + drawnSample;

                                // Estimate the mean...
                                indivLik = myObj->PF_SLAM_computeObservationLikelihoodForParticle(
                                        PF_options,
                                        index,
                                        *static_cast<const CSensoryFrame*>(observation),
                                        x_predict );

                                MRPT_CHECK_NORMAL_NUMBER(indivLik);
                                vectLiks[q] = indivLik;
                                if ( indivLik > maxLik )
                                {       // Keep the maximum value:
                                        maxLikDraw      = drawnSample;
                                        maxLik          = indivLik;
                                }
                        }

                        // This is done to avoid floating point overflow!!
                        //      average_lik    =      \sum(e^liks)   * e^maxLik  /     N
                        // log( average_lik  ) = log( \sum(e^liks) ) + maxLik   - log( N )
                        double avrgLogLik = math::averageLogLikelihood( vectLiks );

                        // Save into the object:
                        myObj->m_pfAuxiliaryPFOptimal_estimatedProb[index] = avrgLogLik; // log( accum / N );
                        myObj->m_pfAuxiliaryPFOptimal_maxLikelihood[index] = maxLik;

                        if (PF_options.pfAuxFilterOptimal_MLE)
                                myObj->m_pfAuxiliaryPFOptimal_maxLikDrawnMovement[index] = maxLikDraw;

                        // and compute the resulting probability of this particle:
                        // ------------------------------------------------------------
                        return myObj->m_partdata.m_particles[index].log_w + myObj->m_pfAuxiliaryPFOptimal_estimatedProb[index];

                        MRPT_END
                } // end of PF_SLAM_particlesEvaluator_AuxPFOptimal


                /**  Compute w[i]·p(z_t | mu_t^i), with mu_t^i being
                  *    the mean of the new robot pose
                  *
                  * \param action MUST be a "const CPose3D*"
                  * \param observation MUST be a "const CSensoryFrame*"
                  */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                double  PF_implementation<PARTICLE_TYPE>::PF_SLAM_particlesEvaluator_AuxPFStandard(
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const CParticleFilterCapable    *obj,
                        size_t                                  index,
                        const void                              *action,
                        const void                              *observation )
                {
                        MRPT_START

                        const PF_implementation<PARTICLE_TYPE> *myObj = reinterpret_cast<const PF_implementation<PARTICLE_TYPE>*>( obj );

                        // Take the previous particle weight:
                        const double cur_logweight = myObj->m_partdata.m_particles[index].log_w;
                        const CPose3D oldPose = *myObj->getLastPose(index);

                        if (!PF_options.pfAuxFilterStandard_FirstStageWeightsMonteCarlo)
                        {
                                // Just use the mean:
                                // , take the mean of the posterior density:
                                CPose3D  x_predict;
                                x_predict.composeFrom( oldPose, *static_cast<const CPose3D*>(action) );

                                // and compute the obs. likelihood:
                                // --------------------------------------------
                                myObj->m_pfAuxiliaryPFStandard_estimatedProb[index] = myObj->PF_SLAM_computeObservationLikelihoodForParticle(
                                        PF_options, index,
                                        *static_cast<const CSensoryFrame*>(observation), x_predict );

                                // Combined log_likelihood: Previous weight * obs_likelihood:
                                return cur_logweight + myObj->m_pfAuxiliaryPFStandard_estimatedProb[index];
                        }
                        else
                        {
                                // Do something similar to in Optimal sampling:
                                // Compute the quantity:
                                //     w[i]·p(zt|z^{t-1},x^{[i],t-1})
                                // As the Monte-Carlo approximation of the integral over all posible $x_t$.
                                // --------------------------------------------
                                double  indivLik, maxLik= -1e300;
                                CPose3D maxLikDraw;
                                size_t  N = PF_options.pfAuxFilterOptimal_MaximumSearchSamples;
                                ASSERT_(N>1)

                                vector_double   vectLiks(N,0);          // The vector with the individual log-likelihoods.
                                CPose3D         drawnSample;
                                for (size_t q=0;q<N;q++)
                                {
                                        myObj->m_movementDrawer.drawSample(drawnSample);
                                        CPose3D x_predict = oldPose + drawnSample;

                                        // Estimate the mean...
                                        indivLik = myObj->PF_SLAM_computeObservationLikelihoodForParticle(
                                                PF_options,
                                                index,
                                                *static_cast<const CSensoryFrame*>(observation),
                                                x_predict );

                                        MRPT_CHECK_NORMAL_NUMBER(indivLik);
                                        vectLiks[q] = indivLik;
                                        if ( indivLik > maxLik )
                                        {       // Keep the maximum value:
                                                maxLikDraw      = drawnSample;
                                                maxLik          = indivLik;
                                        }
                                }

                                // This is done to avoid floating point overflow!!
                                //      average_lik    =      \sum(e^liks)   * e^maxLik  /     N
                                // log( average_lik  ) = log( \sum(e^liks) ) + maxLik   - log( N )
                                double avrgLogLik = math::averageLogLikelihood( vectLiks );

                                // Save into the object:
                                myObj->m_pfAuxiliaryPFStandard_estimatedProb[index] = avrgLogLik; // log( accum / N );

                                myObj->m_pfAuxiliaryPFOptimal_maxLikelihood[index] = maxLik;
                                if (PF_options.pfAuxFilterOptimal_MLE)
                                        myObj->m_pfAuxiliaryPFOptimal_maxLikDrawnMovement[index] = maxLikDraw;

                                // and compute the resulting probability of this particle:
                                // ------------------------------------------------------------
                                return cur_logweight + myObj->m_pfAuxiliaryPFOptimal_estimatedProb[index];
                        }
                        MRPT_END
                }

                // USE_OPTIMAL_SAMPLING:
                //   true -> PF_SLAM_implementation_pfAuxiliaryPFOptimal
                //  false -> PF_SLAM_implementation_pfAuxiliaryPFStandard
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                void PF_implementation<PARTICLE_TYPE>::PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal(
                        const CActionCollection * actions,
                        const CSensoryFrame             * sf,
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        const TKLDParams &KLD_options,
                        const bool USE_OPTIMAL_SAMPLING  )
                {
                        MRPT_START
                        typedef std::set<BINTYPE,typename BINTYPE::lt_operator>         TSetStateSpaceBins;

                        const size_t M = m_partdata.m_particles.size();

                        // ----------------------------------------------------------------------
                        //        We can execute optimal PF only when we have both, an action, and
                        //     a valid observation from which to compute the likelihood:
                        //   Accumulate odometry/actions until we have a valid observation, then
                        //    process them simultaneously.
                        // ----------------------------------------------------------------------
                        if (!PF_SLAM_implementation_gatherActionsCheckBothActObs<BINTYPE>(actions,sf))
                                return; // Nothing we can do here...
                        // OK, we have m_movementDrawer loaded and observations...let's roll!


                        // -------------------------------------------------------------------------------
                        //              0) Common part:  Prepare m_particles "draw" and compute "fastDrawSample"
                        // -------------------------------------------------------------------------------
                        // We need the (aproximate) maximum likelihood value for each
                        //  previous particle [i]:
                        //     max{ p( z^t | data^[i], x_(t-1)^[i], u_(t) ) }
                        //

                        m_pfAuxiliaryPFOptimal_maxLikelihood.assign(M, INVALID_LIKELIHOOD_VALUE);
                        m_pfAuxiliaryPFOptimal_maxLikDrawnMovement.resize(M);
//                      if (USE_OPTIMAL_SAMPLING)
                                m_pfAuxiliaryPFOptimal_estimatedProb.resize(M);
//                      else
                                m_pfAuxiliaryPFStandard_estimatedProb.resize(M);

                        // Pass the "mean" robot movement to the "weights" computing function:
                        CPose3D meanRobotMovement;
                        m_movementDrawer.getSamplingMean3D(meanRobotMovement);

                        // Prepare data for executing "fastDrawSample"
                        typedef PF_implementation<PARTICLE_TYPE> TMyClass; // Use this longer declaration to avoid errors in old GCC.
                        CParticleFilterCapable::TParticleProbabilityEvaluator funcOpt = &TMyClass::template PF_SLAM_particlesEvaluator_AuxPFOptimal<BINTYPE>;
                        CParticleFilterCapable::TParticleProbabilityEvaluator funcStd = &TMyClass::template PF_SLAM_particlesEvaluator_AuxPFStandard<BINTYPE>;

                        m_pfc.prepareFastDrawSample(
                                PF_options,
                                USE_OPTIMAL_SAMPLING ? funcOpt : funcStd,
                                &meanRobotMovement,
                                sf );

                        // For USE_OPTIMAL_SAMPLING=1,  m_pfAuxiliaryPFOptimal_maxLikelihood is now computed.

                        if (USE_OPTIMAL_SAMPLING && PF_options.verbose)
                        {
                                printf("[prepareFastDrawSample] max      (log) = %10.06f\n",  math::maximum(m_pfAuxiliaryPFOptimal_estimatedProb) );
                                printf("[prepareFastDrawSample] max-mean (log) = %10.06f\n", -math::mean(m_pfAuxiliaryPFOptimal_estimatedProb) + math::maximum(m_pfAuxiliaryPFOptimal_estimatedProb) );
                                printf("[prepareFastDrawSample] max-min  (log) = %10.06f\n", -math::minimum(m_pfAuxiliaryPFOptimal_estimatedProb) + math::maximum(m_pfAuxiliaryPFOptimal_estimatedProb) );
                        }

                        // Now we have the vector "m_fastDrawProbability" filled out with:
                        //               w[i]·p(zt|z^{t-1},x^{[i],t-1},X)
                        //  where,
                        //
                        //  =========== For USE_OPTIMAL_SAMPLING = true ====================
                        //  X is the robot pose prior (as implemented in
                        //  the aux. function "PF_SLAM_particlesEvaluator_AuxPFOptimal"),
                        //  and also the "m_pfAuxiliaryPFOptimal_maxLikelihood" filled with the maximum lik. values.
                        //
                        //  =========== For USE_OPTIMAL_SAMPLING = false ====================
                        //  X is a single point close to the mean of the robot pose prior (as implemented in
                        //  the aux. function "PF_SLAM_particlesEvaluator_AuxPFStandard").
                        //
                        vector<TPose3D>                 newParticles;
                        vector_double                   newParticlesWeight;
                        vector<size_t>                  newParticlesDerivedFromIdx;

                        // We need the (aproximate) maximum likelihood value for each
                        //  previous particle [i]:
                        //
                        //     max{ p( z^t | data^[i], x_(t-1)^[i], u_(t) ) }
                        //
                        if (PF_options.pfAuxFilterOptimal_MLE)
                                m_pfAuxiliaryPFOptimal_maxLikMovementDrawHasBeenUsed.assign(M, false);

                        const double            maxMeanLik = math::maximum(
                                USE_OPTIMAL_SAMPLING ?
                                        m_pfAuxiliaryPFOptimal_estimatedProb :
                                        m_pfAuxiliaryPFStandard_estimatedProb );

                        if ( !PF_options.adaptiveSampleSize )
                        {
                                // ----------------------------------------------------------------------
                                //                                              1) FIXED SAMPLE SIZE VERSION
                                // ----------------------------------------------------------------------
                                newParticles.resize(M);
                                newParticlesWeight.resize(M);
                                newParticlesDerivedFromIdx.resize(M);

                                const bool doResample = m_pfc.ESS() < PF_options.BETA;

                                for (size_t i=0;i<M;i++)
                                {
                                        size_t k;

                                        // Generate a new particle:
                                        //   (a) Draw a "t-1" m_particles' index:
                                        // ----------------------------------------------------------------
                                        if (doResample)
                                                        k = m_pfc.fastDrawSample(PF_options);           // Based on weights of last step only!
                                        else    k = i;

                                        // Do one rejection sampling step:
                                        // ---------------------------------------------
                                        CPose3D         newPose;
                                        double          newParticleLogWeight;
                                        PF_SLAM_aux_perform_one_rejection_sampling_step<BINTYPE>(
                                                USE_OPTIMAL_SAMPLING,doResample,maxMeanLik,
                                                k,
                                                sf,PF_options,
                                                newPose, newParticleLogWeight);

                                        // Insert the new particle
                                        newParticles[i] = newPose;
                                        newParticlesDerivedFromIdx[i] = k;
                                        newParticlesWeight[i] = newParticleLogWeight;

                                } // for i
                        } // end fixed sample size
                        else
                        {
                                // -------------------------------------------------------------------------------------------------
                                //                                                              2) ADAPTIVE SAMPLE SIZE VERSION
                                //
                                //      Implementation of Dieter Fox's KLD algorithm
                                //              JLBC (3/OCT/2006)
                                // -------------------------------------------------------------------------------------------------
                                // The new particle set:
                                newParticles.clear();
                                newParticlesWeight.clear();
                                newParticlesDerivedFromIdx.clear();

                                // ------------------------------------------------------------------------------
                                // 2.1) PRELIMINARY STAGE: Build a list of pairs<TPathBin,vector_uint> with the
                                //      indexes of m_particles that fall into each multi-dimensional-path bins
                                //      //The bins will be saved into "stateSpaceBinsLastTimestep", and the list
                                //      //of corresponding m_particles (in the last timestep), in "stateSpaceBinsLastTimestepParticles"
                                //  - Added JLBC (01/DEC/2006)
                                // ------------------------------------------------------------------------------
                                TSetStateSpaceBins                      stateSpaceBinsLastTimestep;
                                std::vector<vector_uint>        stateSpaceBinsLastTimestepParticles;
                                typename BASE::CParticleList::iterator          partIt;
                                unsigned int    partIndex;

                                printf( "[FIXED_SAMPLING] Computing...");
                                for (partIt = m_partdata.m_particles.begin(),partIndex=0; partIt!=m_partdata.m_particles.end(); ++partIt,++partIndex)
                                {
                                        // Load the bin from the path data:
                                        BINTYPE p;
                                        KLF_loadBinFromParticle<PARTICLE_TYPE,BINTYPE>(p, KLD_options,partIt->d );

                                        // Is it a new bin?
                                        typename TSetStateSpaceBins::iterator posFound=stateSpaceBinsLastTimestep.find(p);
                                        if ( posFound == stateSpaceBinsLastTimestep.end() )
                                        {       // Yes, create a new pair <bin,index_list> in the list:
                                                stateSpaceBinsLastTimestep.insert( p );
                                                stateSpaceBinsLastTimestepParticles.push_back( vector_uint(1,partIndex) );
                                        }
                                        else
                                        { // No, add the particle's index to the existing entry:
                                                const size_t idx = std::distance(stateSpaceBinsLastTimestep.begin(),posFound);
                                                stateSpaceBinsLastTimestepParticles[idx].push_back( partIndex );
                                        }
                                }
                                printf( "done (%u bins in t-1)\n",(unsigned int)stateSpaceBinsLastTimestep.size());

                                // ------------------------------------------------------------------------------
                                // 2.2)    THE MAIN KLD-BASED DRAW SAMPLING LOOP
                                // ------------------------------------------------------------------------------
                                double          delta_1 = 1.0 - KLD_options.KLD_delta;
                                double          epsilon_1 = 0.5 / KLD_options.KLD_epsilon;
                                bool            doResample = m_pfc.ESS() < 0.5;
                                //double        maxLik = math::maximum(m_pfAuxiliaryPFOptimal_maxLikelihood); // For normalization purposes only

                                // The desired dynamic number of m_particles (to be modified dynamically below):
                                const size_t  minNumSamples_KLD = max((size_t)KLD_options.KLD_minSampleSize, (size_t)round(KLD_options.KLD_minSamplesPerBin*stateSpaceBinsLastTimestep.size()) );
                                size_t Nx = minNumSamples_KLD ;

                                const size_t Np1 = m_partdata.m_particles.size();
                                vector_size_t oldPartIdxsStillNotPropragated(Np1);  // Use a list since we'll use "erase" a lot here.
                                for (size_t k=0;k<Np1;k++) oldPartIdxsStillNotPropragated[k]=k; //.push_back(k);

                                const size_t Np = stateSpaceBinsLastTimestepParticles.size();
                                vector_size_t permutationPathsAuxVector(Np);
                                for (size_t k=0;k<Np;k++) permutationPathsAuxVector[k]=k;

                                // Instead of picking randomly from "permutationPathsAuxVector", we can shuffle it now just once,
                                // then pick in sequence from the tail and resize the container:
                                std::random_shuffle(
                                        permutationPathsAuxVector.begin(),
                                        permutationPathsAuxVector.end(),
                                        mrpt::random::random_generator_for_STL );

                                size_t k = 0;
                                size_t N = 0;

                                TSetStateSpaceBins              stateSpaceBins;

                                do // "N" is the index of the current "new particle":
                                {
                                        // Generate a new particle:
                                        //
                                        //   (a) Propagate the last set of m_particles, and only if the
                                        //       desired number of m_particles in this step is larger,
                                        //       perform a UNIFORM sampling from the last set. In this way
                                        //       the new weights can be computed in the same way for all m_particles.
                                        // ---------------------------------------------------------------------------
                                        if (doResample)
                                        {
                                                k = m_pfc.fastDrawSample(PF_options);           // Based on weights of last step only!
                                        }
                                        else
                                        {
                                                // Assure that at least one particle per "discrete-path" is taken (if the
                                                //  number of samples allows it)
                                                if (permutationPathsAuxVector.size())
                                                {
                                                        const size_t idxBinSpacePath = *permutationPathsAuxVector.rbegin();
                                                        permutationPathsAuxVector.resize(permutationPathsAuxVector.size()-1);

                                                        const size_t idx = randomGenerator.drawUniform32bit() % stateSpaceBinsLastTimestepParticles[idxBinSpacePath].size();
                                                        k = stateSpaceBinsLastTimestepParticles[idxBinSpacePath][idx];
                                                        ASSERT_(k<m_partdata.m_particles.size());

                                                        // Also erase it from the other permutation vector list:
                                                        oldPartIdxsStillNotPropragated.erase(std::find(oldPartIdxsStillNotPropragated.begin(),oldPartIdxsStillNotPropragated.end(),k));
                                                }
                                                else
                                                {
                                                        // Select a particle from the previous set with a UNIFORM distribution,
                                                        // in such a way we will assign each particle the updated weight accounting
                                                        // for its last weight.
                                                        // The first "old_N" m_particles will be drawn using a uniform random selection
                                                        // without repetitions:
                                                        //
                                                        // Select a index from "oldPartIdxsStillNotPropragated" and remove it from the list:
                                                        if (oldPartIdxsStillNotPropragated.size())
                                                        {
                                                                const size_t idx = randomGenerator.drawUniform32bit() % oldPartIdxsStillNotPropragated.size();
                                                                vector_size_t::iterator it = oldPartIdxsStillNotPropragated.begin() + idx; //advance(it,idx);
                                                                k = *it;
                                                                oldPartIdxsStillNotPropragated.erase(it);
                                                        }
                                                        else
                                                        {
                                                                // N>N_old -> Uniformly draw index:
                                                                k = randomGenerator.drawUniform32bit() % m_partdata.m_particles.size();
                                                        }
                                                }
                                        }

                                        // Do one rejection sampling step:
                                        // ---------------------------------------------
                                        CPose3D         newPose;
                                        double          newParticleLogWeight;
                                        PF_SLAM_aux_perform_one_rejection_sampling_step<BINTYPE>(
                                                USE_OPTIMAL_SAMPLING,doResample,maxMeanLik,
                                                k,
                                                sf,PF_options,
                                                newPose, newParticleLogWeight);

                                        // Insert the new particle
                                        newParticles.push_back( newPose );
                                        newParticlesDerivedFromIdx.push_back( k );
                                        newParticlesWeight.push_back(newParticleLogWeight);

                                        // ----------------------------------------------------------------
                                        // Now, the KLD-sampling dynamic sample size stuff:
                                        //  look if the particle's PATH falls into a new bin or not:
                                        // ----------------------------------------------------------------
                                        BINTYPE p;
                                        const TPose3D  newPose_s = newPose;
                                        KLF_loadBinFromParticle<PARTICLE_TYPE,BINTYPE>( p,KLD_options, m_partdata.m_particles[k].d, &newPose_s );

                                        // -----------------------------------------------------------------------------
                                        // Look for the bin "p" into "stateSpaceBins": If it is not yet into the set,
                                        //  then we may increase the desired particle number:
                                        // -----------------------------------------------------------------------------

                                        // Found?
                                        if ( stateSpaceBins.find(p)==stateSpaceBins.end() )
                                        {
                                                // It falls into a new bin: add to the stateSpaceBins:
                                                stateSpaceBins.insert( p );

                                                // K = K + 1
                                                int K = stateSpaceBins.size();
                                                if ( K>1 )
                                                {
                                                        // Update the number of m_particles!!
                                                        Nx = (size_t) (epsilon_1 * math::chi2inv(delta_1,K-1));
                                                        //printf("k=%u \tn=%u \tNx:%u\n", k, newParticles.size(), Nx);
                                                }
                                        }

                                        N = newParticles.size();

                                } while ((  N < KLD_options.KLD_maxSampleSize &&
                                                        N < max(Nx,minNumSamples_KLD)) ||
                                                        (permutationPathsAuxVector.size() && !doResample) );

                                printf("\n[ADAPTIVE SAMPLE SIZE]  #Bins: %u \t #Particles: %u \t Nx=%u\n", static_cast<unsigned>(stateSpaceBins.size()),static_cast<unsigned>(N), (unsigned)Nx);
                        } // end adaptive sample size


                        // ---------------------------------------------------------------------------------
                        // Substitute old by new particle set:
                        //   Old are in "m_particles"
                        //   New are in "newParticles", "newParticlesWeight","newParticlesDerivedFromIdx"
                        // ---------------------------------------------------------------------------------
                        this->PF_SLAM_implementation_replaceByNewParticleSet(
                                m_partdata.m_particles,
                                newParticles,newParticlesWeight,newParticlesDerivedFromIdx );


                        // In this PF_algorithm, we must do weight normalization by ourselves:
                        m_pfc.normalizeWeights();

                        MRPT_END
                } // end of PF_SLAM_implementation_pfAuxiliaryPFStandardAndOptimal


                /* ------------------------------------------------------------------------
                                                        PF_SLAM_aux_perform_one_rejection_sampling_step
                   ------------------------------------------------------------------------ */
                template <class PARTICLE_TYPE>
                template <class BINTYPE>
                void PF_implementation<PARTICLE_TYPE>::PF_SLAM_aux_perform_one_rejection_sampling_step(
                        const bool              USE_OPTIMAL_SAMPLING,
                        const bool              doResample,
                        const double    maxMeanLik,
                        size_t    k, // The particle from the old set "m_particles[]"
                        const CSensoryFrame             * sf,
                        const CParticleFilter::TParticleFilterOptions &PF_options,
                        CPose3D                 & out_newPose,
                        double                  & out_newParticleLogWeight)
                {
                        // ADD-ON: If the 'm_pfAuxiliaryPFOptimal_estimatedProb[k]' is **extremelly** low relative to the other m_particles,
                        //  resample only this particle with a copy of another one, uniformly:
                        while ( ( (USE_OPTIMAL_SAMPLING ? m_pfAuxiliaryPFOptimal_estimatedProb[k] : m_pfAuxiliaryPFStandard_estimatedProb[k] )
                                                -maxMeanLik) < -PF_options.max_loglikelihood_dyn_range )
                        {
                                // Select another 'k' uniformly:
                                k = randomGenerator.drawUniform32bit() % m_partdata.m_particles.size();
                                if (PF_options.verbose) cout << "[PF_implementation] Warning: Discarding very unlikely particle" << endl;
                        }

                        const CPose3D oldPose = *getLastPose(k);        // Get the current pose of the k'th particle

                        //   (b) Rejection-sampling: Draw a new robot pose from x[k],
                        //       and accept it with probability p(zk|x) / maxLikelihood:
                        // ----------------------------------------------------------------
                        double poseLogLik;
                        if ( PF_SLAM_implementation_skipRobotMovement() )
                        {
                                // The first robot pose in the SLAM execution: All m_particles start
                                // at the same point (this is the lowest bound of subsequent uncertainty):
                                out_newPose = oldPose;
                                poseLogLik = 0;
                        }
                        else
                        {
                                CPose3D movementDraw;
                                if (!USE_OPTIMAL_SAMPLING)
                                {       // APF:
                                        m_movementDrawer.drawSample( movementDraw );
                                        out_newPose.composeFrom(oldPose, movementDraw); // newPose = oldPose + movementDraw;
                                        // Compute likelihood:
                                        poseLogLik = PF_SLAM_computeObservationLikelihoodForParticle(PF_options, k,*sf,out_newPose);
                                }
                                else
                                {       // Optimal APF with rejection sampling:
                                        // Rejection-sampling:
                                        double acceptanceProb;
                                        int             timeout = 0;
                                        const int       maxTries=10000;
                                        double      bestTryByNow_loglik= -std::numeric_limits<double>::max();
                                        TPose3D         bestTryByNow_pose;
                                        do
                                        {
                                                // Draw new robot pose:
                                                if (PF_options.pfAuxFilterOptimal_MLE && !m_pfAuxiliaryPFOptimal_maxLikMovementDrawHasBeenUsed[k])
                                                {       // No! first take advantage of a good drawn value, but only once!!
                                                        m_pfAuxiliaryPFOptimal_maxLikMovementDrawHasBeenUsed[k] = true;
                                                        movementDraw = CPose3D( m_pfAuxiliaryPFOptimal_maxLikDrawnMovement[k] );
                                                }
                                                else
                                                {
                                                        // Draw new robot pose:
                                                        m_movementDrawer.drawSample( movementDraw );
                                                }

                                                out_newPose.composeFrom(oldPose, movementDraw); // out_newPose = oldPose + movementDraw;

                                                // Compute acceptance probability:
                                                poseLogLik = PF_SLAM_computeObservationLikelihoodForParticle(PF_options, k,*sf,out_newPose);

                                                if (poseLogLik>bestTryByNow_loglik)
                                                {
                                                        bestTryByNow_loglik = poseLogLik;
                                                        bestTryByNow_pose = out_newPose;
                                                }

                                                double ratioLikLik = std::exp( poseLogLik - m_pfAuxiliaryPFOptimal_maxLikelihood[k] );
                                                acceptanceProb = std::min( 1.0, ratioLikLik );

                                                if ( ratioLikLik > 1)
                                                {
                                                        m_pfAuxiliaryPFOptimal_maxLikelihood[k] = poseLogLik; //  :'-( !!!
                                                        //acceptanceProb = 0;           // Keep searching or keep this one?
                                                }
                                        } while ( ++timeout<maxTries && acceptanceProb < randomGenerator.drawUniform(0.0,0.999) );

                                        if (timeout>=maxTries)
                                        {
                                                out_newPose = bestTryByNow_pose;
                                                poseLogLik = bestTryByNow_loglik;
                                                if (PF_options.verbose) cout << "[PF_implementation] Warning: timeout in rejection sampling." << endl;
                                        }

                                }

                                // And its weight:
                                if (USE_OPTIMAL_SAMPLING)
                                {       // Optimal PF
                                        if (doResample)
                                                out_newParticleLogWeight = 0;  // By definition of our optimal PF, all samples have identical weights.
                                        else
                                        {
                                                const double weightFact = m_pfAuxiliaryPFOptimal_estimatedProb[k] * PF_options.powFactor;
                                                out_newParticleLogWeight = m_partdata.m_particles[k].log_w + weightFact;
                                        }
                                }
                                else
                                {       // APF:
                                        const double weightFact = (poseLogLik-m_pfAuxiliaryPFStandard_estimatedProb[k]) * PF_options.powFactor;
                                        if (doResample)
                                                        out_newParticleLogWeight = weightFact;
                                        else    out_newParticleLogWeight = weightFact + m_partdata.m_particles[k].log_w;
                                }

                        }
                        // Done.
                } // end PF_SLAM_aux_perform_one_rejection_sampling_step


        } // end namespace
} // end namespace

#endif

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