Point Cloud Library (PCL)  1.9.1-dev
multi_ransac.h
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37 
38 #pragma once
39 
40 #include <pcl/cuda/sample_consensus/sac.h>
41 #include <pcl/cuda/sample_consensus/sac_model.h>
42 
43 namespace pcl
44 {
45  namespace cuda
46  {
47  /** \brief @b RandomSampleConsensus represents an implementation of the
48  * RANSAC (RAndom SAmple Consensus) algorithm, as described in: "Random
49  * Sample Consensus: A Paradigm for Model Fitting with Applications to Image
50  * Analysis and Automated Cartography", Martin A. Fischler and Robert C. Bolles,
51  * Comm. Of the ACM 24: 381–395, June 1981.
52  * \author Radu Bogdan Rusu
53  */
54  template <template <typename> class Storage>
56  {
66 
67  using SampleConsensusModelPtr = typename SampleConsensusModel<Storage>::Ptr;
68  using Coefficients = typename SampleConsensusModel<Storage>::Coefficients;
69  using Hypotheses = typename SampleConsensusModel<Storage>::Hypotheses;
70 
71  using Indices = typename SampleConsensusModel<Storage>::Indices;
72  using IndicesPtr = typename SampleConsensusModel<Storage>::IndicesPtr;
73  using IndicesConstPtr = typename SampleConsensusModel<Storage>::IndicesConstPtr;
74 
75  public:
76  /** \brief RANSAC (RAndom SAmple Consensus) main constructor
77  * \param model a Sample Consensus model
78  */
79  MultiRandomSampleConsensus (const SampleConsensusModelPtr &model) :
80  SampleConsensus<Storage> (model),
81  min_coverage_percent_ (0.9),
82  max_batches_ (5),
83  iterations_per_batch_ (1000)
84  {
85  // Maximum number of trials before we give up.
86  max_iterations_ = 10000;
87  }
88 
89  /** \brief RANSAC (RAndom SAmple Consensus) main constructor
90  * \param model a Sample Consensus model
91  * \param threshold distance to model threshold
92  */
93  MultiRandomSampleConsensus (const SampleConsensusModelPtr &model, double threshold) :
94  SampleConsensus<Storage> (model, threshold)
95  {
96  // Maximum number of trials before we give up.
97  max_iterations_ = 10000;
98  }
99 
100  /** \brief Compute the actual model and find the inliers
101  * \param debug_verbosity_level enable/disable on-screen debug
102  * information and set the verbosity level
103  */
104  bool
105  computeModel (int debug_verbosity_level = 0);
106 
107  /** \brief how much (in percent) of the point cloud should be covered?
108  * If it is not possible to find enough planes, it will stop according to the regular ransac criteria
109  */
110  void
111  setMinimumCoverage (float percent)
112  {
113  min_coverage_percent_ = percent;
114  }
115 
116  /** \brief Sets the maximum number of batches that should be processed.
117  * Every Batch computes up to iterations_per_batch_ models and verifies them.
118  * If planes with a sufficiently high total inlier count are found earlier, the
119  * actual number of batch runs might be lower.
120  */
121  void
122  setMaximumBatches (int max_batches)
123  {
124  max_batches_ = max_batches_;
125  }
126 
127  /** \brief Sets the maximum number of batches that should be processed.
128  * Every Batch computes up to max_iterations_ models and verifies them.
129  * If planes with a sufficiently high total inlier count are found earlier, the
130  * actual number of batch runs might be lower.
131  */
132  void
133  setIerationsPerBatch(int iterations_per_batch)
134  {
135  iterations_per_batch_ = iterations_per_batch;
136  }
137 
138  inline std::vector<IndicesPtr>
139  getAllInliers () { return all_inliers_; }
140 
141  inline std::vector<int>
142  getAllInlierCounts () { return all_inlier_counts_; }
143 
144  /** \brief Return the model coefficients of the best model found so far.
145  */
146  inline std::vector<float4>
148  {
149  return all_model_coefficients_;
150  }
151 
152  /** \brief Return the model coefficients of the best model found so far.
153  */
154  inline std::vector<float3>
156  {
157  return all_model_centroids_;
158  }
159 
160  private:
161  float min_coverage_percent_;
162  unsigned int max_batches_;
163  unsigned int iterations_per_batch_;
164 
165  /** \brief The vector of the centroids of our models computed directly from the models found. */
166  std::vector<float3> all_model_centroids_;
167 
168  /** \brief The vector of coefficients of our models computed directly from the models found. */
169  std::vector<float4> all_model_coefficients_;
170 
171  std::vector<IndicesPtr> all_inliers_;
172  std::vector<int> all_inlier_counts_;
173  };
174 
175  } // namespace
176 } // namespace
MultiRandomSampleConsensus(const SampleConsensusModelPtr &model, double threshold)
RANSAC (RAndom SAmple Consensus) main constructor.
Definition: multi_ransac.h:93
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
std::vector< IndicesPtr > getAllInliers()
Definition: multi_ransac.h:139
boost::shared_ptr< SampleConsensusModel > Ptr
Definition: sac_model.h:94
void setMaximumBatches(int max_batches)
Sets the maximum number of batches that should be processed.
Definition: multi_ransac.h:122
std::vector< int > getAllInlierCounts()
Definition: multi_ransac.h:142
typename Storage< int >::type Indices
Definition: sac_model.h:97
boost::shared_ptr< const typename Storage< int >::type > IndicesConstPtr
Definition: sac_model.h:99
bool computeModel(int debug_verbosity_level=0)
Compute the actual model and find the inliers.
boost::shared_ptr< typename Storage< int >::type > IndicesPtr
Definition: sac_model.h:98
MultiRandomSampleConsensus(const SampleConsensusModelPtr &model)
RANSAC (RAndom SAmple Consensus) main constructor.
Definition: multi_ransac.h:79
int max_iterations_
Maximum number of iterations before giving up.
Definition: sac.h:195
typename Storage< float >::type Coefficients
Definition: sac_model.h:101
RandomSampleConsensus represents an implementation of the RANSAC (RAndom SAmple Consensus) algorithm...
Definition: multi_ransac.h:55
void setMinimumCoverage(float percent)
how much (in percent) of the point cloud should be covered? If it is not possible to find enough plan...
Definition: multi_ransac.h:111
typename Storage< float4 >::type Hypotheses
Definition: sac_model.h:105
std::vector< float3 > getAllModelCentroids()
Return the model coefficients of the best model found so far.
Definition: multi_ransac.h:155
std::vector< float4 > getAllModelCoefficients()
Return the model coefficients of the best model found so far.
Definition: multi_ransac.h:147
void setIerationsPerBatch(int iterations_per_batch)
Sets the maximum number of batches that should be processed.
Definition: multi_ransac.h:133