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<div class="title">CParticleFilter.h</div> </div>
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<a href="_c_particle_filter_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">/* +---------------------------------------------------------------------------+</span>
<a name="l00002"></a>00002 <span class="comment"> | The Mobile Robot Programming Toolkit (MRPT) C++ library |</span>
<a name="l00003"></a>00003 <span class="comment"> | |</span>
<a name="l00004"></a>00004 <span class="comment"> | http://www.mrpt.org/ |</span>
<a name="l00005"></a>00005 <span class="comment"> | |</span>
<a name="l00006"></a>00006 <span class="comment"> | Copyright (C) 2005-2011 University of Malaga |</span>
<a name="l00007"></a>00007 <span class="comment"> | |</span>
<a name="l00008"></a>00008 <span class="comment"> | This software was written by the Machine Perception and Intelligent |</span>
<a name="l00009"></a>00009 <span class="comment"> | Robotics Lab, University of Malaga (Spain). |</span>
<a name="l00010"></a>00010 <span class="comment"> | Contact: Jose-Luis Blanco <jlblanco@ctima.uma.es> |</span>
<a name="l00011"></a>00011 <span class="comment"> | |</span>
<a name="l00012"></a>00012 <span class="comment"> | This file is part of the MRPT project. |</span>
<a name="l00013"></a>00013 <span class="comment"> | |</span>
<a name="l00014"></a>00014 <span class="comment"> | MRPT is free software: you can redistribute it and/or modify |</span>
<a name="l00015"></a>00015 <span class="comment"> | it under the terms of the GNU General Public License as published by |</span>
<a name="l00016"></a>00016 <span class="comment"> | the Free Software Foundation, either version 3 of the License, or |</span>
<a name="l00017"></a>00017 <span class="comment"> | (at your option) any later version. |</span>
<a name="l00018"></a>00018 <span class="comment"> | |</span>
<a name="l00019"></a>00019 <span class="comment"> | MRPT is distributed in the hope that it will be useful, |</span>
<a name="l00020"></a>00020 <span class="comment"> | but WITHOUT ANY WARRANTY; without even the implied warranty of |</span>
<a name="l00021"></a>00021 <span class="comment"> | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the |</span>
<a name="l00022"></a>00022 <span class="comment"> | GNU General Public License for more details. |</span>
<a name="l00023"></a>00023 <span class="comment"> | |</span>
<a name="l00024"></a>00024 <span class="comment"> | You should have received a copy of the GNU General Public License |</span>
<a name="l00025"></a>00025 <span class="comment"> | along with MRPT. If not, see <http://www.gnu.org/licenses/>. |</span>
<a name="l00026"></a>00026 <span class="comment"> | |</span>
<a name="l00027"></a>00027 <span class="comment"> +---------------------------------------------------------------------------+ */</span>
<a name="l00028"></a>00028 <span class="preprocessor">#ifndef CPARTICLEFILTER_H</span>
<a name="l00029"></a>00029 <span class="preprocessor"></span><span class="preprocessor">#define CPARTICLEFILTER_H</span>
<a name="l00030"></a>00030 <span class="preprocessor"></span>
<a name="l00031"></a>00031 <span class="preprocessor">#include <<a class="code" href="utils__defs_8h.html">mrpt/utils/utils_defs.h</a>></span>
<a name="l00032"></a>00032 <span class="preprocessor">#include <<a class="code" href="_c_debug_output_capable_8h.html">mrpt/utils/CDebugOutputCapable.h</a>></span>
<a name="l00033"></a>00033 <span class="preprocessor">#include <<a class="code" href="_c_loadable_options_8h.html">mrpt/utils/CLoadableOptions.h</a>></span>
<a name="l00034"></a>00034
<a name="l00035"></a><a class="code" href="namespacemrpt.html">00035</a> <span class="keyword">namespace </span>mrpt
<a name="l00036"></a>00036 {
<a name="l00037"></a><a class="code" href="namespacemrpt_1_1slam.html">00037</a> <span class="keyword">namespace </span>slam
<a name="l00038"></a>00038 {
<a name="l00039"></a>00039 <span class="keyword">class </span><a class="code" href="classmrpt_1_1slam_1_1_c_action_collection.html" title="Declares a class for storing a collection of robot actions.">CActionCollection</a>;
<a name="l00040"></a>00040 <span class="keyword">class </span><a class="code" href="classmrpt_1_1slam_1_1_c_sensory_frame.html" title="Declares a class for storing a "sensory frame", a set of "observations" taken by the robot approximat...">CSensoryFrame</a>;
<a name="l00041"></a>00041 }
<a name="l00042"></a>00042 <span class="comment"></span>
<a name="l00043"></a>00043 <span class="comment"> /** The namespace for Bayesian filtering algorithm: different particle filters and Kalman filter algorithms. \ingroup mrpt_base_grp</span>
<a name="l00044"></a>00044 <span class="comment"> */</span>
<a name="l00045"></a><a class="code" href="namespacemrpt_1_1bayes.html">00045</a> <span class="keyword">namespace </span>bayes
<a name="l00046"></a>00046 {
<a name="l00047"></a>00047 <span class="keyword">class </span><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter_capable.html" title="This virtual class defines the interface that any particles based PDF class must implement in order t...">CParticleFilterCapable</a>;
<a name="l00048"></a>00048 <span class="comment"></span>
<a name="l00049"></a>00049 <span class="comment"> /** This class acts as a common interface to the different interfaces (see CParticleFilter::TParticleFilterAlgorithm) any bayes::CParticleFilterCapable class can implement: it is the invoker of particle filter algorithms.</span>
<a name="l00050"></a>00050 <span class="comment"> * The particle filter is executed on a probability density function (PDF) described by a CParticleFilterCapable object, passed in the constructor or alternatively through the CParticleFilter::executeOn method.<br></span>
<a name="l00051"></a>00051 <span class="comment"> *</span>
<a name="l00052"></a>00052 <span class="comment"> * For a complete example and further details, see the <a href="http://www.mrpt.org/Particle_Filter_Tutorial" >Particle Filter tutorial</a>.</span>
<a name="l00053"></a>00053 <span class="comment"> *</span>
<a name="l00054"></a>00054 <span class="comment"> * The basic SIR algorithm (pfStandardProposal) consists of:</span>
<a name="l00055"></a>00055 <span class="comment"> * - Execute a prediction with the given "action".</span>
<a name="l00056"></a>00056 <span class="comment"> * - Update the weights of the particles using the likelihood of the "observation".</span>
<a name="l00057"></a>00057 <span class="comment"> * - Normalize weights.</span>
<a name="l00058"></a>00058 <span class="comment"> * - Perform resampling if the ESS is below the threshold options.BETA.</span>
<a name="l00059"></a>00059 <span class="comment"> *</span>
<a name="l00060"></a>00060 <span class="comment"> * \ingroup mrpt_base_grp</span>
<a name="l00061"></a>00061 <span class="comment"> * \sa mrpt::poses::CPoseParticlesPDF</span>
<a name="l00062"></a>00062 <span class="comment"> */</span>
<a name="l00063"></a>00063 <span class="keyword">class </span><a class="code" href="base_2include_2mrpt_2base_2link__pragmas_8h.html#a6045fa0129b1a3d6c8bf895470e66574">BASE_IMPEXP</a> CParticleFilter : <span class="keyword">public</span> mrpt::utils::<a class="code" href="classmrpt_1_1utils_1_1_c_debug_output_capable.html" title="This base class provides a common printf-like method to send debug information to std::cout...">CDebugOutputCapable</a>
<a name="l00064"></a>00064 {
<a name="l00065"></a>00065 <span class="keyword">public</span>:
<a name="l00066"></a>00066 <span class="comment"></span>
<a name="l00067"></a>00067 <span class="comment"> /** Defines different types of particle filter algorithms.</span>
<a name="l00068"></a>00068 <span class="comment"> * The defined SIR implementations are:</span>
<a name="l00069"></a>00069 <span class="comment"> * - pfStandardProposal: Standard proposal distribution + weights according to likelihood function.</span>
<a name="l00070"></a>00070 <span class="comment"> * - pfAuxiliaryPFStandard: An auxiliary PF using the standard proposal distribution.</span>
<a name="l00071"></a>00071 <span class="comment"> * - pfOptimalProposal: Use the optimal proposal distribution (where available!, usually this will perform approximations)</span>
<a name="l00072"></a>00072 <span class="comment"> * - pfAuxiliaryPFOptimal: Use the optimal proposal and a auxiliary particle filter (see <a href="http://www.mrpt.org/Paper:An_Optimal_Filtering_Algorithm_for_Non-Parametric_Observation_Models_in_Robot_Localization_(ICRA_2008)" >paper</a>).</span>
<a name="l00073"></a>00073 <span class="comment"> *</span>
<a name="l00074"></a>00074 <span class="comment"> * See the theoretical discussion in <a href="http://www.mrpt.org/Resampling_Schemes" >resampling schemes</a>.</span>
<a name="l00075"></a>00075 <span class="comment"> */</span>
<a name="l00076"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7">00076</a> <span class="keyword">enum</span> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7" title="Defines different types of particle filter algorithms.">TParticleFilterAlgorithm</a>
<a name="l00077"></a>00077 {
<a name="l00078"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7a09e2ccb57a3e839af011e663fbe3a667">00078</a> pfStandardProposal = 0,
<a name="l00079"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7ad2207ecd8d1e42ebee8b2744972603c0">00079</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7ad2207ecd8d1e42ebee8b2744972603c0">pfAuxiliaryPFStandard</a>,
<a name="l00080"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7a4cdfe15977bafe20462beefc906faebf">00080</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7a4cdfe15977bafe20462beefc906faebf">pfOptimalProposal</a>,
<a name="l00081"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7abacc5c8a1d1f0e737ba4e9558fd91de4">00081</a> pfAuxiliaryPFOptimal
<a name="l00082"></a>00082 };
<a name="l00083"></a>00083 <span class="comment"></span>
<a name="l00084"></a>00084 <span class="comment"> /** Defines the different resampling algorithms.</span>
<a name="l00085"></a>00085 <span class="comment"> * The implemented resampling methods are:</span>
<a name="l00086"></a>00086 <span class="comment"> * - prMultinomial (Default): Uses standard select with replacement (draws M random uniform numbers)</span>
<a name="l00087"></a>00087 <span class="comment"> * - prResidual: The residual or "remainder" method.</span>
<a name="l00088"></a>00088 <span class="comment"> * - prStratified: The stratified resampling, where a uniform sample is drawn for each of M subdivisions of the range (0,1].</span>
<a name="l00089"></a>00089 <span class="comment"> * - prSystematic: A single uniform sample is drawn in the range (0,1/M].</span>
<a name="l00090"></a>00090 <span class="comment"> *</span>
<a name="l00091"></a>00091 <span class="comment"> * See the theoretical discussion in <a href="http://www.mrpt.org/Resampling_Schemes" >resampling schemes</a>.</span>
<a name="l00092"></a>00092 <span class="comment"> */</span>
<a name="l00093"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154c">00093</a> <span class="keyword">enum</span> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154c" title="Defines the different resampling algorithms.">TParticleResamplingAlgorithm</a>
<a name="l00094"></a>00094 {
<a name="l00095"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca6b2c858dc3a007653b879c350a48dca0">00095</a> prMultinomial = 0,
<a name="l00096"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca718819b4633782b7d6adc9cc2ebba4bc">00096</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca718819b4633782b7d6adc9cc2ebba4bc">prResidual</a>,
<a name="l00097"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca8747cc68f954ecaea9adddfff1c5f877">00097</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca8747cc68f954ecaea9adddfff1c5f877">prStratified</a>,
<a name="l00098"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154ca610313e8aae9c864e57a6ce98272112f">00098</a> prSystematic
<a name="l00099"></a>00099 };
<a name="l00100"></a>00100 <span class="comment"></span>
<a name="l00101"></a>00101 <span class="comment"> /** The configuration of a particle filter.</span>
<a name="l00102"></a>00102 <span class="comment"> */</span>
<a name="l00103"></a>00103 <span class="keyword">struct </span><a class="code" href="base_2include_2mrpt_2base_2link__pragmas_8h.html#a6045fa0129b1a3d6c8bf895470e66574">BASE_IMPEXP</a> TParticleFilterOptions : <span class="keyword">public</span> mrpt::utils::<a class="code" href="classmrpt_1_1utils_1_1_c_loadable_options.html" title="This is a virtual base class for sets of options than can be loaded from and/or saved to configuratio...">CLoadableOptions</a>
<a name="l00104"></a>00104 {
<a name="l00105"></a>00105 <span class="keyword">public</span>:<span class="comment"></span>
<a name="l00106"></a>00106 <span class="comment"> /** Initilization of default parameters</span>
<a name="l00107"></a>00107 <span class="comment"> */</span>
<a name="l00108"></a>00108 TParticleFilterOptions();
<a name="l00109"></a>00109 <span class="comment"></span>
<a name="l00110"></a>00110 <span class="comment"> /** See mrpt::utils::CLoadableOptions</span>
<a name="l00111"></a>00111 <span class="comment"> */</span>
<a name="l00112"></a>00112 <span class="keywordtype">void</span> loadFromConfigFile(
<a name="l00113"></a>00113 <span class="keyword">const</span> <a class="code" href="classmrpt_1_1utils_1_1_c_config_file_base.html" title="This class allows loading and storing values and vectors of different types from a configuration text...">mrpt::utils::CConfigFileBase</a> &source,
<a name="l00114"></a>00114 <span class="keyword">const</span> <a class="code" href="classstd_1_1string.html" title="STL class.">std::string</a> &section);
<a name="l00115"></a>00115 <span class="comment"></span>
<a name="l00116"></a>00116 <span class="comment"> /** See mrpt::utils::CLoadableOptions</span>
<a name="l00117"></a>00117 <span class="comment"> */</span>
<a name="l00118"></a>00118 <span class="keywordtype">void</span> dumpToTextStream(<a class="code" href="classmrpt_1_1utils_1_1_c_stream.html" title="This base class is used to provide a unified interface to files,memory buffers,..Please see the deriv...">mrpt::utils::CStream</a> &out) <span class="keyword">const</span>;
<a name="l00119"></a>00119 <span class="comment"></span>
<a name="l00120"></a>00120 <span class="comment"> /** A flag that indicates whether the CParticleFilterCapable object should perform adative sample size (default=false).</span>
<a name="l00121"></a>00121 <span class="comment"> */</span>
<a name="l00122"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#ae1373ee71fb2a0131f448042291d85f0">00122</a> <span class="keywordtype">bool</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#ae1373ee71fb2a0131f448042291d85f0" title="A flag that indicates whether the CParticleFilterCapable object should perform adative sample size (d...">adaptiveSampleSize</a>;
<a name="l00123"></a>00123 <span class="comment"></span>
<a name="l00124"></a>00124 <span class="comment"> /** The resampling of particles will be performed when ESS (in range [0,1]) < BETA (default is 0.5)</span>
<a name="l00125"></a>00125 <span class="comment"> */</span>
<a name="l00126"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#ac905494cf568dd8291428306661acaf2">00126</a> <span class="keywordtype">double</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#ac905494cf568dd8291428306661acaf2" title="The resampling of particles will be performed when ESS (in range [0,1]) < BETA (default is 0...">BETA</a>;
<a name="l00127"></a>00127 <span class="comment"></span>
<a name="l00128"></a>00128 <span class="comment"> /** The initial number of particles in the filter (it can change only if adaptiveSampleSize=true) (default=1)</span>
<a name="l00129"></a>00129 <span class="comment"> */</span>
<a name="l00130"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#af476e1fb9a9106d4751368bb30ef516a">00130</a> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#af476e1fb9a9106d4751368bb30ef516a" title="The initial number of particles in the filter (it can change only if adaptiveSampleSize=true) (defaul...">sampleSize</a>;
<a name="l00131"></a>00131 <span class="comment"></span>
<a name="l00132"></a>00132 <span class="comment"> /** In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal" (and in "CParticleFilter::pfAuxiliaryPFStandard" only if pfAuxFilterStandard_FirstStageWeightsMonteCarlo = true) the number of samples for searching the maximum likelihood value and also to estimate the "first stage weights" (see papers!) (default=100)</span>
<a name="l00133"></a>00133 <span class="comment"> */</span>
<a name="l00134"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a4054623753f30e03e99e94e85c3ff84d">00134</a> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a4054623753f30e03e99e94e85c3ff84d" title="In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal" (and in "CParticleFilter::pfAuxiliaryPFStand...">pfAuxFilterOptimal_MaximumSearchSamples</a>;
<a name="l00135"></a>00135 <span class="comment"></span>
<a name="l00136"></a>00136 <span class="comment"> /** An optional step to "smooth" dramatic changes in the observation model to affect the variance of the particle weights, eg weight*=likelihood^powFactor (default=1 = no effects).</span>
<a name="l00137"></a>00137 <span class="comment"> */</span>
<a name="l00138"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a6ad1667bac7faa62a2376c83527c0414">00138</a> <span class="keywordtype">double</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a6ad1667bac7faa62a2376c83527c0414" title="An optional step to "smooth" dramatic changes in the observation model to affect the variance of the ...">powFactor</a>;
<a name="l00139"></a>00139 <span class="comment"></span>
<a name="l00140"></a>00140 <span class="comment"> /** The PF algorithm to use (default=pfStandardProposal) See TParticleFilterAlgorithm for the posibilities.</span>
<a name="l00141"></a>00141 <span class="comment"> */</span>
<a name="l00142"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a9735fe24fc688a65092fb0fe1aee792f">00142</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#ab53101e2ac73154a4044a9562d20acb7" title="Defines different types of particle filter algorithms.">TParticleFilterAlgorithm</a> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a9735fe24fc688a65092fb0fe1aee792f" title="The PF algorithm to use (default=pfStandardProposal) See TParticleFilterAlgorithm for the posibilitie...">PF_algorithm</a>;
<a name="l00143"></a>00143 <span class="comment"></span>
<a name="l00144"></a>00144 <span class="comment"> /** The resampling algorithm to use (default=prMultinomial).</span>
<a name="l00145"></a>00145 <span class="comment"> */</span>
<a name="l00146"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a245527d93f30ada0f61bc1a2663a31d7">00146</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a5a2e5c42fba6719b5f2f4b8fa1f4154c" title="Defines the different resampling algorithms.">TParticleResamplingAlgorithm</a> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a245527d93f30ada0f61bc1a2663a31d7" title="The resampling algorithm to use (default=prMultinomial).">resamplingMethod</a>;
<a name="l00147"></a>00147
<a name="l00148"></a>00148 <span class="comment"></span>
<a name="l00149"></a>00149 <span class="comment"> /** Only for PF_algorithm=pfAuxiliaryPFOptimal: If a given particle has a max_likelihood (from the a-priori estimate) below the maximum from all the samples - max_loglikelihood_dyn_range, then the particle is directly discarded.</span>
<a name="l00150"></a>00150 <span class="comment"> * This is done to assure that the rejection sampling doesn't get stuck in an infinite loop trying to get an acceptable sample.</span>
<a name="l00151"></a>00151 <span class="comment"> * Default = 15 (in logarithmic likelihood)</span>
<a name="l00152"></a>00152 <span class="comment"> */</span>
<a name="l00153"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a8613b2d409e3a78de42dba07f6cdcec3">00153</a> <span class="keywordtype">double</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a8613b2d409e3a78de42dba07f6cdcec3" title="Only for PF_algorithm=pfAuxiliaryPFOptimal: If a given particle has a max_likelihood (from the a-prio...">max_loglikelihood_dyn_range</a>;
<a name="l00154"></a>00154 <span class="comment"></span>
<a name="l00155"></a>00155 <span class="comment"> /** Only for PF_algorithm==pfAuxiliaryPFStandard:</span>
<a name="l00156"></a>00156 <span class="comment"> * If false, the APF will predict the first stage weights just at the mean of the prior of the next time step.</span>
<a name="l00157"></a>00157 <span class="comment"> * If true, these weights will be estimated as described in the papers for the "pfAuxiliaryPFOptimal" method, i.e. through a monte carlo simulation.</span>
<a name="l00158"></a>00158 <span class="comment"> * In that case, "pfAuxFilterOptimal_MaximumSearchSamples" is the number of MC samples used.</span>
<a name="l00159"></a>00159 <span class="comment"> */</span>
<a name="l00160"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a16a5d97f9246477c32e742cc23e20a74">00160</a> <span class="keywordtype">bool</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a16a5d97f9246477c32e742cc23e20a74" title="Only for PF_algorithm==pfAuxiliaryPFStandard: If false, the APF will predict the first stage weights ...">pfAuxFilterStandard_FirstStageWeightsMonteCarlo</a>;
<a name="l00161"></a>00161
<a name="l00162"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a33e80606a59d313f3338f71367010957">00162</a> <span class="keywordtype">bool</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a33e80606a59d313f3338f71367010957" title="Enable extra messages for each PF iteration (Default=false)">verbose</a>; <span class="comment">//!< Enable extra messages for each PF iteration (Default=false)</span>
<a name="l00163"></a>00163 <span class="comment"></span><span class="comment"></span>
<a name="l00164"></a>00164 <span class="comment"> /** (Default=false) In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal", if set to true, do not perform rejection sampling, but just the most-likely (ML) particle found in the preliminary weight-determination stage.</span>
<a name="l00165"></a>00165 <span class="comment"> */</span>
<a name="l00166"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a99406b39b2963dce127a86634f13871f">00166</a> <span class="keywordtype">bool</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html#a99406b39b2963dce127a86634f13871f" title="(Default=false) In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal", if set to true...">pfAuxFilterOptimal_MLE</a>;
<a name="l00167"></a>00167 };
<a name="l00168"></a>00168
<a name="l00169"></a>00169 <span class="comment"></span>
<a name="l00170"></a>00170 <span class="comment"> /** Statistics for being returned from the "execute" method.</span>
<a name="l00171"></a>00171 <span class="comment"> */</span>
<a name="l00172"></a>00172 <span class="keyword">struct </span><a class="code" href="base_2include_2mrpt_2base_2link__pragmas_8h.html#a6045fa0129b1a3d6c8bf895470e66574">BASE_IMPEXP</a> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html" title="Statistics for being returned from the "execute" method.">TParticleFilterStats</a>
<a name="l00173"></a>00173 {
<a name="l00174"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#aba9c7bcf2d0d323d42d54cfd09ec8fa1">00174</a> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#aba9c7bcf2d0d323d42d54cfd09ec8fa1">TParticleFilterStats</a>() : ESS_beforeResample(0), weightsVariance_beforeResample (0) { }
<a name="l00175"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#a368e6433831ae1248f634052909b30fa">00175</a> <span class="keywordtype">double</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#a368e6433831ae1248f634052909b30fa">ESS_beforeResample</a>;
<a name="l00176"></a><a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#a930f6d126496dfdd09f47ea137ef3c6e">00176</a> <span class="keywordtype">double</span> <a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_stats.html#a930f6d126496dfdd09f47ea137ef3c6e">weightsVariance_beforeResample</a>;
<a name="l00177"></a>00177 };
<a name="l00178"></a>00178 <span class="comment"></span>
<a name="l00179"></a>00179 <span class="comment"> /** Default constructor.</span>
<a name="l00180"></a>00180 <span class="comment"> * After creating the PF object, set the options in CParticleFilter::m_options, then execute steps through CParticleFilter::executeOn.</span>
<a name="l00181"></a>00181 <span class="comment"> */</span>
<a name="l00182"></a>00182 CParticleFilter( );
<a name="l00183"></a>00183
<a name="l00184"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a7ecd2a20c5b04b931829e0c81c9c8878">00184</a> <span class="keyword">virtual</span> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a7ecd2a20c5b04b931829e0c81c9c8878">~CParticleFilter</a>() {}
<a name="l00185"></a>00185 <span class="comment"></span>
<a name="l00186"></a>00186 <span class="comment"> /** Executes a complete prediction + update step of the selected particle filtering algorithm.</span>
<a name="l00187"></a>00187 <span class="comment"> * The member CParticleFilter::m_options must be set before calling this to settle the algorithm parameters.</span>
<a name="l00188"></a>00188 <span class="comment"> *</span>
<a name="l00189"></a>00189 <span class="comment"> * \param obj The object representing the probability distribution function (PDF) which apply the particle filter algorithm to.</span>
<a name="l00190"></a>00190 <span class="comment"> * \param action A pointer to an action in the form of a CActionCollection, or NULL if there is no action.</span>
<a name="l00191"></a>00191 <span class="comment"> * \param observation A pointer to observations in the form of a CSensoryFrame, or NULL if there is no observation.</span>
<a name="l00192"></a>00192 <span class="comment"> * \param stats An output structure for gathering statistics of the particle filter execution, or set to NULL if you do not need it (see CParticleFilter::TParticleFilterStats).</span>
<a name="l00193"></a>00193 <span class="comment"> *</span>
<a name="l00194"></a>00194 <span class="comment"> * \sa CParticleFilterCapable, executeOn</span>
<a name="l00195"></a>00195 <span class="comment"> */</span>
<a name="l00196"></a>00196 <span class="keywordtype">void</span> executeOn(
<a name="l00197"></a>00197 <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter_capable.html" title="This virtual class defines the interface that any particles based PDF class must implement in order t...">CParticleFilterCapable</a> &obj,
<a name="l00198"></a>00198 <span class="keyword">const</span> <a class="code" href="classmrpt_1_1slam_1_1_c_action_collection.html" title="Declares a class for storing a collection of robot actions.">mrpt::slam::CActionCollection</a> *action,
<a name="l00199"></a>00199 <span class="keyword">const</span> <a class="code" href="classmrpt_1_1slam_1_1_c_sensory_frame.html" title="Declares a class for storing a "sensory frame", a set of "observations" taken by the robot approximat...">mrpt::slam::CSensoryFrame</a> *observation,
<a name="l00200"></a>00200 TParticleFilterStats *stats = NULL);
<a name="l00201"></a>00201
<a name="l00202"></a>00202 <span class="comment"></span>
<a name="l00203"></a>00203 <span class="comment"> /** The options to be used in the PF, must be set before executing any step of the particle filter.</span>
<a name="l00204"></a>00204 <span class="comment"> */</span>
<a name="l00205"></a><a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a428e336c70f1e76eb987b901feec0a51">00205</a> CParticleFilter<a class="code" href="structmrpt_1_1bayes_1_1_c_particle_filter_1_1_t_particle_filter_options.html" title="The configuration of a particle filter.">::TParticleFilterOptions</a> <a class="code" href="classmrpt_1_1bayes_1_1_c_particle_filter.html#a428e336c70f1e76eb987b901feec0a51" title="The options to be used in the PF, must be set before executing any step of the particle filter...">m_options</a>;
<a name="l00206"></a>00206
<a name="l00207"></a>00207 }; <span class="comment">// End of class def.</span>
<a name="l00208"></a>00208
<a name="l00209"></a>00209 } <span class="comment">// end namespace</span>
<a name="l00210"></a>00210 } <span class="comment">// end namespace</span>
<a name="l00211"></a>00211 <span class="preprocessor">#endif</span>
</pre></div></div>
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