Name

Localization_MRPT

Brief

Localization MRPT Component

Description

Localization RT-component using Monte Carlo Localization. This RT-component uses MRPT. See http://mrpt.org. To build this RT-component, install mrpt-1.2.0.

License

GPLv3

Image

Language

C++

URL

https://github.com/sugarsweetrobotics/Localization_MRPT.git

Platform

[‘win’, ‘ubuntu’]

Data In Ports

Name	Type	Unit	Description
range	RTC::RangeData		Range Sensor Data (usually, LiDAR)
odometry	RTC::TimedPose2D		Robot position. Usually, estimated with encoders.

Data Out Ports

Name	Type	Unit	Description
estimatedPose	RTC::TimedPose2D		Estimated pose output.

Service Ports

mapServer

Name	Direction	Type	Description
mapServer	Required	RTC::OGMapServer	Map Server Interface

Configurations

Name	Type	Default	Unit	Description
initial_pose_x	string	0	meter	Initial pose of robot
initial_pose_y	float	0	meter	Initial pose of robot
initial_pose_phi	float	0		Initial pose of robot
initial_particle_min_x	float	-0.3	meter	minimum value range of initial particle position (This is added to initial position)
initial_particle_max_x	float	0.3	meter	maximum value range of initial particle position (This is added to initial position)
initial_particle_min_y	float	-0.3	meter	minimum value range of initial particle position (This is added to initial position)
initial_particle_max_y	float	0.3	meter	maximum value range of initial particle position (This is added to initial position)
initial_particle_min_phi	float	-0.3		minimum value range of initial particle position (This is added to initial position)
initial_particle_max_phi	float	0.3	radian	maximum value range of initial particle position (This is added to initial position)
initial_particle_count	string	1000		Initial particle count
poseTimeOut	float	3.0		Timeout threshold [sec] of odometry port.
motion_model	string	Gausian		Type name for Motion model. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_alpha1	float	0.01		Parameter of motion_model. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_alpha2	float	0.05729		Parameter of motion_model. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_alpha3	float	0.01745		Parameter of motion_model. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_alpha4	float	0.05		Parameter of motion_model. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_std_XY	float	0.01		Parameter for Motion model. if motion_model is Gausian, this is Sigma_min if motion_model is Thrun, this is additional noise parameter. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
motion_std_PHI	float	0.003490		Parameter for Motion model. if motion_model is Gausian, this is Sigma_min if motion_model is Thrun, this is additional noise parameter. http://www.mrpt.org/tutorials/programming/odometry-and-motion-models/probabilistic_motion_models/
LM_likelihoodMethod	string	lmLikelihoodField_Thrun		The selected method to compute an observation likelihood. http://www.mrpt.org/tutorials/programming/maps-for-localization-slam-map-building/occupancy_grids/#42_Observations_likelihood
LM_enableLikelihoodCache	string	true		Enables the usage of a cache of likelihood values (for LF methods), if set to true (default=true).
LM_LF_decimation	int	5		[LikelihoodField] The laser range “sigma” used in computations; Default value = 0.35
LM_LF_stdHit	float	0.35		The laser range “sigma” used in computations; Default value = 0.35
LM_LF_zRandom	float	0.05		Ratios of the hit/random components of the likelihood; Default values=0.05
LM_LF_alternateAverageMethod	string	false		Set this to “true” ot use an alternative method, where the likelihood of the whole range scan is computed by “averaging” of individual ranges, instead of by the “product”. Default = false
LM_LF_zHit	float	0.95		Ratios of the hit/random components of the likelihood; Default values=0.95
LM_LF_maxCorrsDistance	float	0.3		The max. distance for searching correspondences around each sensed point default 0.3
LM_LF_maxRange	float	81	meter	The max. range of the sensor (def=81meters)
LM_MI_exponent	float	2.5		The exponent in the MI likelihood computation. Default value = 2.5
LM_MI_ratio_max_distance	float	1.5		[MI] The ratio for the max. distance used in the MI computation and in the insertion of scans, e.g. if set to 2.0 the MI will use twice the distance that the update distance. def=1.5
LM_MI_skip_rays	float	10		The scan rays decimation: at every N rays, one will be used to compute the MI: def=10
LM_consensus_pow	float	5		The power factor for the likelihood (default=5)
LM_consensus_takeEachRange	int	1		The down-sample ratio of ranges (default=1, consider all the ranges)
LM_rayTracing_stdHit	float	1.0		[rayTracing] The laser range sigma. def=1.0
LM_rayTracing_decimation	int	10		One out of “rayTracing_decimation” rays will be simulated and compared only: set to 1 to use all the sensed ranges. def=10
LM_rayTracing_useDistanceFilter	string	true		If true (default), the rayTracing method will ignore measured ranges shorter than the simulated ones.
PF_algorithm	string	pfStandardProposal		Sequential Importance Resampling – SIR (pfStandardProposal) Standard proposal distribution + weights according to likelihood function. Auxiliary Particle Filter – APF (pfAuxiliaryPFStandard) This method was introduced by Pitt and Shephard in 1999 [1] Optimal Sampling (pfOptimalProposal) Use the exact optimal proposal distribution (where available!, usually this will perform approximations). In the case of the RBPF-SLAM implementation, this method follows [2] Approximate Optimal Sampling (pfAuxiliaryPFOptimal) Use the optimal proposal and a auxiliary particle filter (see paper [3] ). See : http://www.mrpt.org/tutorials/programming/statistics-and-bayes-filtering/particle_filter_algorithms/
PF_resamplingMethod	string	prMultinomial		Setting of resampling method. See: http://www.mrpt.org/tutorials/programming/statistics-and-bayes-filtering/resampling_schemes/
PF_BETA	float	0.5		The resampling of particles will be performed when ESS (in range [0,1]) < BETA (default is 0.5)
PF_powFactor	float	1.0		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).
PF_sampleSize	string	1		The initial number of particles in the filter (it can change only if adaptiveSampleSize=true) (default=1)
PF_adaptiveSampleSize	string	true		A flag that indicates whether the CParticleFilterCapable object should perform adative sample size (default=true).
PF_max_loglikelihood_dyn_range	double	15		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 – This is done to assure that the rejection sampling doesn’t get stuck in an infinite loop trying to get an acceptable sample. Default = 15 (in logarithmic likelihood)
PF_AuxFilterOptimal_MaximumSearchSamples	int	100		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)
PF_AuxFilterStandard_FirstStageWeightsMonteCarlo	string	false		Only for PF_algorithm==pfAuxiliaryPFStandard: If false, the APF will predict the first stage weights just at the mean of the prior of the next time step. If true, these weights will be estimated as described in the papers for the “pfAuxiliaryPFOptimal” method, i.e. through a monte carlo simulation. In that case, “pfAuxFilterOptimal_MaximumSearchSamples” is the number of MC samples used.
PF_AuxFilterOptimal_MLE	string	false		(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.
KLD_binSize_PHI	float	0.01		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_binSize_XY	float	0.01		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_delta	float	0.02		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_epsilon	float	0.02		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_maxSampleSize	int	1000		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_minSampleSize	int	150		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)
KLD_minSamplesPerBin	double	0		Parameters for the KLD adaptive sample size algorithm (see Dieter Fox’s papers)

How To Build

Linux
  $ git clone https://github.com/sugarsweetrobotics/Localization_MRPT.git
  $ cd Localization_MRPT
  $ mkdir build
  $ cd build
  $ cmake ../
  $ make
Windows
  Download ZIP file from the website, and use CMake to generate VC2010 project.
  Then, open the project and build it.

How To Build in wasanbon

  $ wasanbon-admin.py package create Localization_MRPT_test_project # You can also use other preinstalled package 
  $ wasanbon-cd Localization_MRPT_test_project
  $ ./mgr.py repository clone Localization_MRPT
  $ ./mgr.py rtc build Localization_MRPT
  $ ./mgr.py rtc run Localization_MRPT # To Run RTC

Profile Status

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Build Status

Build in Windows

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Build in OSX

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Build in Linux

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