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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

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../../../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.

see also PCLModule.status

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.