Point Cloud Library (PCL)
1.9.1
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RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A. More...
Public Member Functions | |
MultiRandomSampleConsensus (const SampleConsensusModelPtr &model) | |
RANSAC (RAndom SAmple Consensus) main constructor. More... | |
MultiRandomSampleConsensus (const SampleConsensusModelPtr &model, double threshold) | |
RANSAC (RAndom SAmple Consensus) main constructor. More... | |
bool | computeModel (int debug_verbosity_level=0) |
Compute the actual model and find the inliers. More... | |
void | setMinimumCoverage (float percent) |
how much (in percent) of the point cloud should be covered? If it is not possible to find enough planes, it will stop according to the regular ransac criteria More... | |
void | setMaximumBatches (int max_batches) |
Sets the maximum number of batches that should be processed. More... | |
void | setIerationsPerBatch (int iterations_per_batch) |
Sets the maximum number of batches that should be processed. More... | |
std::vector< IndicesPtr > | getAllInliers () |
std::vector< int > | getAllInlierCounts () |
std::vector< float4 > | getAllModelCoefficients () |
Return the model coefficients of the best model found so far. More... | |
std::vector< float3 > | getAllModelCentroids () |
Return the model coefficients of the best model found so far. More... | |
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SampleConsensus (const SampleConsensusModelPtr &model) | |
Constructor for base SAC. More... | |
SampleConsensus (const SampleConsensusModelPtr &model, float threshold) | |
Constructor for base SAC. More... | |
virtual | ~SampleConsensus () |
Destructor for base SAC. More... | |
void | setDistanceThreshold (float threshold) |
Set the distance to model threshold. More... | |
float | getDistanceThreshold () |
Get the distance to model threshold, as set by the user. More... | |
void | setMaxIterations (int max_iterations) |
Set the maximum number of iterations. More... | |
int | getMaxIterations () |
Get the maximum number of iterations, as set by the user. More... | |
void | setProbability (float probability) |
Set the desired probability of choosing at least one sample free from outliers. More... | |
float | getProbability () |
Obtain the probability of choosing at least one sample free from outliers, as set by the user. More... | |
void | getModel (Indices &model) |
Return the best model found so far. More... | |
IndicesPtr | getInliers () |
Return the best set of inliers found so far for this model. More... | |
IndicesPtr | getInliersStencil () |
void | getModelCoefficients (Coefficients &model_coefficients) |
Return the model coefficients of the best model found so far. More... | |
Additional Inherited Members | |
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typedef Storage< float >::type | Coefficients |
typedef boost::shared_ptr < Coefficients > | CoefficientsPtr |
typedef boost::shared_ptr < const Coefficients > | CoefficientsConstPtr |
typedef boost::shared_ptr < SampleConsensus > | Ptr |
typedef boost::shared_ptr < const SampleConsensus > | ConstPtr |
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SampleConsensusModelPtr | sac_model_ |
The underlying data model used (what is it that we attempt to search for). More... | |
Indices | model_ |
The model found after the last computeModel () as point cloud indices. More... | |
IndicesPtr | inliers_ |
The indices of the points that were chosen as inliers after the last call. More... | |
IndicesPtr | inliers_stencil_ |
Coefficients | model_coefficients_ |
The coefficients of our model computed directly from the model found. More... | |
float | probability_ |
Desired probability of choosing at least one sample free from outliers. More... | |
int | iterations_ |
Total number of internal loop iterations that we've done so far. More... | |
float | threshold_ |
Distance to model threshold. More... | |
int | max_iterations_ |
Maximum number of iterations before giving up. More... | |
RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography", Martin A.
Fischler and Robert C. Bolles, Comm. Of the ACM 24: 381–395, June 1981.
Definition at line 56 of file multi_ransac.h.
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inline |
RANSAC (RAndom SAmple Consensus) main constructor.
model | a Sample Consensus model |
Definition at line 80 of file multi_ransac.h.
References pcl::cuda::SampleConsensus< Storage >::max_iterations_.
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inline |
RANSAC (RAndom SAmple Consensus) main constructor.
model | a Sample Consensus model |
threshold | distance to model threshold |
Definition at line 94 of file multi_ransac.h.
References pcl::cuda::SampleConsensus< Storage >::max_iterations_.
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virtual |
Compute the actual model and find the inliers.
debug_verbosity_level | enable/disable on-screen debug information and set the verbosity level |
Implements pcl::cuda::SampleConsensus< Storage >.
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Definition at line 143 of file multi_ransac.h.
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Definition at line 140 of file multi_ransac.h.
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Return the model coefficients of the best model found so far.
Definition at line 156 of file multi_ransac.h.
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Return the model coefficients of the best model found so far.
Definition at line 148 of file multi_ransac.h.
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Sets the maximum number of batches that should be processed.
Every Batch computes up to max_iterations_ models and verifies them. If planes with a sufficiently high total inlier count are found earlier, the actual number of batch runs might be lower.
Definition at line 134 of file multi_ransac.h.
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Sets the maximum number of batches that should be processed.
Every Batch computes up to iterations_per_batch_ models and verifies them. If planes with a sufficiently high total inlier count are found earlier, the actual number of batch runs might be lower.
Definition at line 123 of file multi_ransac.h.
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how much (in percent) of the point cloud should be covered? If it is not possible to find enough planes, it will stop according to the regular ransac criteria
Definition at line 112 of file multi_ransac.h.