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CParticleFilter.h
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3  | http://www.mrpt.org/ |
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5  | Copyright (c) 2005-2017, Individual contributors, see AUTHORS file |
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8  +---------------------------------------------------------------------------+ */
9 #ifndef CPARTICLEFILTER_H
10 #define CPARTICLEFILTER_H
11 
12 #include <mrpt/utils/core_defs.h>
15 
16 namespace mrpt
17 {
18  namespace obs { class CSensoryFrame; class CActionCollection; }
19 
20  /** The namespace for Bayesian filtering algorithm: different particle filters and Kalman filter algorithms. \ingroup mrpt_base_grp
21  */
22  namespace bayes
23  {
25 
26  /** 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.
27  * 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>
28  *
29  * For a complete example and further details, see the <a href="http://www.mrpt.org/Particle_Filter_Tutorial" >Particle Filter tutorial</a>.
30  *
31  * The basic SIR algorithm (pfStandardProposal) consists of:
32  * - Execute a prediction with the given "action".
33  * - Update the weights of the particles using the likelihood of the "observation".
34  * - Normalize weights.
35  * - Perform resampling if the ESS is below the threshold options.BETA.
36  *
37  * \ingroup mrpt_base_grp
38  * \sa mrpt::poses::CPoseParticlesPDF
39  */
40  class BASE_IMPEXP CParticleFilter : public mrpt::utils::COutputLogger
41  {
42  public:
43 
44  /** Defines different types of particle filter algorithms.
45  * The defined SIR implementations are:
46  * - pfStandardProposal: Standard proposal distribution + weights according to likelihood function.
47  * - pfAuxiliaryPFStandard: An auxiliary PF using the standard proposal distribution.
48  * - pfOptimalProposal: Use the optimal proposal distribution (where available!, usually this will perform approximations)
49  * - 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>).
50  *
51  * See the theoretical discussion in <a href="http://www.mrpt.org/Resampling_Schemes" >resampling schemes</a>.
52  */
54  {
55  pfStandardProposal = 0,
58  pfAuxiliaryPFOptimal
59  };
60 
61  /** Defines the different resampling algorithms.
62  * The implemented resampling methods are:
63  * - prMultinomial (Default): Uses standard select with replacement (draws M random uniform numbers)
64  * - prResidual: The residual or "remainder" method.
65  * - prStratified: The stratified resampling, where a uniform sample is drawn for each of M subdivisions of the range (0,1].
66  * - prSystematic: A single uniform sample is drawn in the range (0,1/M].
67  *
68  * See the theoretical discussion in <a href="http://www.mrpt.org/Resampling_Schemes" >resampling schemes</a>.
69  */
71  {
72  prMultinomial = 0,
75  prSystematic
76  };
77 
78  /** The configuration of a particle filter.
79  */
81  {
82  public:
83  TParticleFilterOptions(); //!< Initilization of default parameters
84  void loadFromConfigFile(const mrpt::utils::CConfigFileBase &source,const std::string &section) MRPT_OVERRIDE; // See base docs
85  void dumpToTextStream(mrpt::utils::CStream &out) const MRPT_OVERRIDE; // See base docs
86 
87  bool adaptiveSampleSize; //!< A flag that indicates whether the CParticleFilterCapable object should perform adative sample size (default=false).
88  double BETA; //!< The resampling of particles will be performed when ESS (in range [0,1]) < BETA (default is 0.5)
89  unsigned int sampleSize; //!< The initial number of particles in the filter (it can change only if adaptiveSampleSize=true) (default=1)
90 
91  /** 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)
92  */
94  double powFactor; //!< 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).
95  TParticleFilterAlgorithm PF_algorithm; //!< The PF algorithm to use (default=pfStandardProposal) See TParticleFilterAlgorithm for the posibilities.
96  TParticleResamplingAlgorithm resamplingMethod; //!< The resampling algorithm to use (default=prMultinomial).
97 
98  /** 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.
99  * This is done to assure that the rejection sampling doesn't get stuck in an infinite loop trying to get an acceptable sample.
100  * Default = 15 (in logarithmic likelihood)
101  */
103 
104  /** Only for PF_algorithm==pfAuxiliaryPFStandard:
105  * If false, the APF will predict the first stage weights just at the mean of the prior of the next time step.
106  * If true, these weights will be estimated as described in the papers for the "pfAuxiliaryPFOptimal" method, i.e. through a monte carlo simulation.
107  * In that case, "pfAuxFilterOptimal_MaximumSearchSamples" is the number of MC samples used.
108  */
110 
111  bool pfAuxFilterOptimal_MLE; //!< (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.
112  };
113 
114  /** Statistics for being returned from the "execute" method. */
116  {
117  TParticleFilterStats() : ESS_beforeResample(0), weightsVariance_beforeResample (0) { }
120  };
121 
122  /** Default constructor.
123  * After creating the PF object, set the options in CParticleFilter::m_options, then execute steps through CParticleFilter::executeOn.
124  */
125  CParticleFilter();
126 
127  virtual ~CParticleFilter() {}
128 
129  /** Executes a complete prediction + update step of the selected particle filtering algorithm.
130  * The member CParticleFilter::m_options must be set before calling this to settle the algorithm parameters.
131  *
132  * \param obj The object representing the probability distribution function (PDF) which apply the particle filter algorithm to.
133  * \param action A pointer to an action in the form of a CActionCollection, or NULL if there is no action.
134  * \param observation A pointer to observations in the form of a CSensoryFrame, or NULL if there is no observation.
135  * \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).
136  *
137  * \sa CParticleFilterCapable, executeOn
138  */
139  void executeOn(
141  const mrpt::obs::CActionCollection *action,
142  const mrpt::obs::CSensoryFrame *observation,
143  TParticleFilterStats *stats = NULL);
144 
145 
146  /** The options to be used in the PF, must be set before executing any step of the particle filter.
147  */
149 
150  }; // End of class def.
151 
152  } // end namespace
153 } // end namespace
154 #endif
#define MRPT_OVERRIDE
C++11 "override" for virtuals:
unsigned int pfAuxFilterOptimal_MaximumSearchSamples
In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal" (and in "CParticleFilter::pfAuxiliaryPFStand...
Statistics for being returned from the "execute" method.
Declares a class for storing a collection of robot actions.
This class allows loading and storing values and vectors of different types from a configuration text...
This base class is used to provide a unified interface to files,memory buffers,..Please see the deriv...
Definition: CStream.h:38
TParticleResamplingAlgorithm
Defines the different resampling algorithms.
TParticleResamplingAlgorithm resamplingMethod
The resampling algorithm to use (default=prMultinomial).
Declares a class for storing a "sensory frame", a set of "observations" taken by the robot approximat...
GLhandleARB obj
Definition: glew.h:3276
This virtual class defines the interface that any particles based PDF class must implement in order t...
This class acts as a common interface to the different interfaces (see CParticleFilter::TParticleFilt...
bool pfAuxFilterStandard_FirstStageWeightsMonteCarlo
Only for PF_algorithm==pfAuxiliaryPFStandard: If false, the APF will predict the first stage weights ...
double BETA
The resampling of particles will be performed when ESS (in range [0,1]) < BETA (default is 0...
This is the global namespace for all Mobile Robot Programming Toolkit (MRPT) libraries.
TParticleFilterAlgorithm
Defines different types of particle filter algorithms.
GLsizei const GLcharARB ** string
Definition: glew.h:3293
double max_loglikelihood_dyn_range
Only for PF_algorithm=pfAuxiliaryPFOptimal: If a given particle has a max_likelihood (from the a-prio...
double powFactor
An optional step to "smooth" dramatic changes in the observation model to affect the variance of the ...
The configuration of a particle filter.
unsigned int sampleSize
The initial number of particles in the filter (it can change only if adaptiveSampleSize=true) (defaul...
bool pfAuxFilterOptimal_MLE
(Default=false) In the algorithm "CParticleFilter::pfAuxiliaryPFOptimal", if set to true...
CParticleFilter::TParticleFilterOptions m_options
The options to be used in the PF, must be set before executing any step of the particle filter...
TParticleFilterAlgorithm PF_algorithm
The PF algorithm to use (default=pfStandardProposal) See TParticleFilterAlgorithm for the posibilitie...
GLsizei GLsizei GLchar * source
Definition: glew.h:1739
This is a virtual base class for sets of options than can be loaded from and/or saved to configuratio...
bool adaptiveSampleSize
A flag that indicates whether the CParticleFilterCapable object should perform adative sample size (d...



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