PCLSampleConsensus¶

MLModule¶

genre	`PCLSampleConsensus`
author	`Wolf Spindler`
package	`FMEstable/PCL`
dll	`MLPCLSampleConsensus`
definition	MLPCLSampleConsensus.def
see also	`PCLSampleConsensusModels`
inherits from	`PCLModule`
keywords	`detect`, `search`, `find`, `models`, `registration`, `RANSAC`, `Randomized`, `MLESAC`, `Maximum`, `Likelihood`, `Estimator`, `RMSAC`, `M-estimator`, `RRANSAC`, `MSAC`, `LMedS`, `Least`, `Median`, `of`, `Squares`, `PROSAC`

Purpose¶

Applies a Sample Consensus Model created in a PCLSampleConsensusModels module to the input point cloud and tries to search the model in it with the any of the approaches referenced in pcl::MaximumLikelihoodSampleConsensus, pcl::RandomizedMEstimatorSampleConsensus, pcl::RandomizedRandomSampleConsensus, pcl::MEstimatorSampleConsensus, pcl::LeastMedianSquares, pcl::ProgressiveSampleConsensus, or pcl::RandomSampleConsensus.

Tips¶

The Distance Threshold given as std::numeric_limits<double>::max() from the default class instance often does not stable fine. It is recommended to set a much smaller one for successful operations.

Windows¶

Default Panel¶

../../../Projects/MLPCLSampleConsensus/Modules/mhelp/Images/Screenshots/PCLSampleConsensus._default.png

Input Fields¶

inputPCLObject0¶

name: inputPCLObject0, type: MLBase¶: Connect the point cloud in which the model is searched which is retrieved from the connected PCLSampleConsensusModels module.

inputSacMLModule¶

name: inputSacMLModule, type: MLBase¶: Expects the connection of a PCLSampleConsensusModels output connector PCLSampleConsensusModels.outputSacMLModuleBase to this module to provide a creator for Sample Consensus models.

Output Fields¶

outputPCLObject0¶

name: outputPCLObject0, type: MLBase¶: The output point cloud will be subset of the input point cloud which matches as good as possible the model created by the connected PCLSampleConsensusModels, or if the model could not be found it can be empty. If no input point cloud or PCLSampleConsensusModels are provides then the point cloud can be NULL. Indices of source points (in other words the found inliers) are also provided in the output base object.

Parameter Fields¶

Field Index¶

Distance Threshold: Double

Em Iterations: Integer

Fraction Nr Pretest: Double

Max Iterations: Integer

Model Coefficients: String

Probability: Double

Sac Type: Enum

Status: String

Visible Fields¶

Status¶

name: status, type: String, persistent: no¶: Shows status information about processing results, or in case of errors, some information about reasons.

Sac Type¶

name: sacType, type: Enum, default: RandomSampleConsensus¶: Selects which of the SampleConsensus algorithms is to be used.

Values:

Title	Name	Description
Random Sample Consensus	RandomSampleConsensus	Selects the RANSAC (RAndom SAmple Consensus) algorithm. See pcl::RandomSampleConsensus for details.
Maximum Likelihood Sample Consensus	MaximumLikelihoodSampleConsensus	Selects the MLESAC (Maximum Likelihood Estimator SAmple Consensus) algorithm, see pcl::MaximumLikelihoodSampleConsensus for details.
Randomized MEstimator Sample Consensus	RandomizedMEstimatorSampleConsensus	Selects the RMSAC (Randomized M-estimator SAmple Consensus) algorithm, see pcl::RandomizedMEstimatorSampleConsensus for details.
Randomized Random Sample Consensus	RandomizedRandomSampleConsensus	Selects the RRANSAC (Randomized RAndom SAmple Consensus) algorithm, see pcl::RandomizedRandomSampleConsensus for details.
MEstimator Sample Consensus	MEstimatorSampleConsensus	Selects the MSAC (M-estimator SAmple Consensus) algorithm, see pcl::MEstimatorSampleConsensus for details.
Least Median Sample Consensus	LeastMedianSampleConsensus	Selects the LMedS (Least Median of Squares) algorithm, see pcl::LeastMedianSquares for details.
Progressive Sample Consensus	ProgressiveSampleConsensus	Selects the PROSAC (RAndom SAmple Consensus) algorithm, see pcl::ProgressiveSampleConsensus for details.

Distance Threshold¶

name: distanceThreshold, type: Double, default: 1.79769313486232e+308¶: The distance to model threshold which must be considered in the model search. The default std::numeric_limits<double>::max() from the default class instance often does not stable fine. It is recommended to set a much smaller one for successful operations.

Max Iterations¶

name: maxIterations, type: Integer, default: 1000¶: The maximum number of allowed iterations to find a good result.

Probability¶

name: probability, type: Double, default: 0.99¶: The desired probability of choosing at least one sample free from outliers.

Em Iterations¶

name: emIterations, type: Integer, default: 3¶: Sets the number of EMIterations needed if the algorithms MaximumLikelihoodSampleConsensus is selected in Sac Type.

Fraction Nr Pretest¶

name: fractionNrPretest, type: Double, default: 10¶: Sets the percentage of points to pre-test which is needed if the algorithms RandomizedRandomSampleConsensus or RandomizedMEstimatorSampleConsensus is selected in Sac Type.

Model Coefficients¶

name: modelCoefficients, type: String, persistent: no¶: The coefficients of the best model; the number and interpretation of coefficients depends on the model type.