38 #ifndef PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_
39 #define PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_
41 #include <pcl/cuda/sample_consensus/sac.h>
42 #include <pcl/cuda/sample_consensus/sac_model.h>
55 template <
template <
typename>
class Storage>
82 min_coverage_percent_ (0.9),
84 iterations_per_batch_ (1000)
114 min_coverage_percent_ = percent;
125 max_batches_ = max_batches_;
136 iterations_per_batch_ = iterations_per_batch;
139 inline std::vector<IndicesPtr>
142 inline std::vector<int>
147 inline std::vector<float4>
150 return all_model_coefficients_;
155 inline std::vector<float3>
158 return all_model_centroids_;
162 float min_coverage_percent_;
163 unsigned int max_batches_;
164 unsigned int iterations_per_batch_;
167 std::vector<float3> all_model_centroids_;
170 std::vector<float4> all_model_coefficients_;
172 std::vector<IndicesPtr> all_inliers_;
173 std::vector<int> all_inlier_counts_;
179 #endif //#ifndef PCL_CUDA_SAMPLE_CONSENSUS_RANSAC_H_
boost::shared_ptr< typename Storage< int >::type > IndicesPtr
MultiRandomSampleConsensus(const SampleConsensusModelPtr &model, double threshold)
RANSAC (RAndom SAmple Consensus) main constructor.
This file defines compatibility wrappers for low level I/O functions.
std::vector< IndicesPtr > getAllInliers()
void setMaximumBatches(int max_batches)
Sets the maximum number of batches that should be processed.
boost::shared_ptr< const typename Storage< int >::type > IndicesConstPtr
Storage< float >::type Coefficients
Storage< int >::type Indices
std::vector< int > getAllInlierCounts()
Storage< float4 >::type Hypotheses
boost::shared_ptr< SampleConsensusModel > Ptr
bool computeModel(int debug_verbosity_level=0)
Compute the actual model and find the inliers.
MultiRandomSampleConsensus(const SampleConsensusModelPtr &model)
RANSAC (RAndom SAmple Consensus) main constructor.
int max_iterations_
Maximum number of iterations before giving up.
RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm...
void setMinimumCoverage(float percent)
how much (in percent) of the point cloud should be covered? If it is not possible to find enough plan...
std::vector< float3 > getAllModelCentroids()
Return the model coefficients of the best model found so far.
std::vector< float4 > getAllModelCoefficients()
Return the model coefficients of the best model found so far.
void setIerationsPerBatch(int iterations_per_batch)
Sets the maximum number of batches that should be processed.