Point Cloud Library (PCL)  1.7.1
sac_model_line.h
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40 
41 #ifndef PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_
42 #define PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_
43 
44 #include <pcl/sample_consensus/sac_model.h>
45 #include <pcl/sample_consensus/model_types.h>
46 #include <pcl/common/eigen.h>
47 
48 namespace pcl
49 {
50  /** \brief SampleConsensusModelLine defines a model for 3D line segmentation.
51  * The model coefficients are defined as:
52  * - \b point_on_line.x : the X coordinate of a point on the line
53  * - \b point_on_line.y : the Y coordinate of a point on the line
54  * - \b point_on_line.z : the Z coordinate of a point on the line
55  * - \b line_direction.x : the X coordinate of a line's direction
56  * - \b line_direction.y : the Y coordinate of a line's direction
57  * - \b line_direction.z : the Z coordinate of a line's direction
58  *
59  * \author Radu B. Rusu
60  * \ingroup sample_consensus
61  */
62  template <typename PointT>
64  {
65  public:
69 
73 
74  typedef boost::shared_ptr<SampleConsensusModelLine> Ptr;
75 
76  /** \brief Constructor for base SampleConsensusModelLine.
77  * \param[in] cloud the input point cloud dataset
78  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
79  */
80  SampleConsensusModelLine (const PointCloudConstPtr &cloud, bool random = false)
81  : SampleConsensusModel<PointT> (cloud, random) {};
82 
83  /** \brief Constructor for base SampleConsensusModelLine.
84  * \param[in] cloud the input point cloud dataset
85  * \param[in] indices a vector of point indices to be used from \a cloud
86  * \param[in] random if true set the random seed to the current time, else set to 12345 (default: false)
87  */
88  SampleConsensusModelLine (const PointCloudConstPtr &cloud,
89  const std::vector<int> &indices,
90  bool random = false)
91  : SampleConsensusModel<PointT> (cloud, indices, random) {};
92 
93  /** \brief Empty destructor */
95 
96  /** \brief Check whether the given index samples can form a valid line model, compute the model coefficients from
97  * these samples and store them internally in model_coefficients_. The line coefficients are represented by a
98  * point and a line direction
99  * \param[in] samples the point indices found as possible good candidates for creating a valid model
100  * \param[out] model_coefficients the resultant model coefficients
101  */
102  bool
103  computeModelCoefficients (const std::vector<int> &samples,
104  Eigen::VectorXf &model_coefficients);
105 
106  /** \brief Compute all squared distances from the cloud data to a given line model.
107  * \param[in] model_coefficients the coefficients of a line model that we need to compute distances to
108  * \param[out] distances the resultant estimated squared distances
109  */
110  void
111  getDistancesToModel (const Eigen::VectorXf &model_coefficients,
112  std::vector<double> &distances);
113 
114  /** \brief Select all the points which respect the given model coefficients as inliers.
115  * \param[in] model_coefficients the coefficients of a line model that we need to compute distances to
116  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
117  * \param[out] inliers the resultant model inliers
118  */
119  void
120  selectWithinDistance (const Eigen::VectorXf &model_coefficients,
121  const double threshold,
122  std::vector<int> &inliers);
123 
124  /** \brief Count all the points which respect the given model coefficients as inliers.
125  *
126  * \param[in] model_coefficients the coefficients of a model that we need to compute distances to
127  * \param[in] threshold maximum admissible distance threshold for determining the inliers from the outliers
128  * \return the resultant number of inliers
129  */
130  virtual int
131  countWithinDistance (const Eigen::VectorXf &model_coefficients,
132  const double threshold);
133 
134  /** \brief Recompute the line coefficients using the given inlier set and return them to the user.
135  * @note: these are the coefficients of the line model after refinement (eg. after SVD)
136  * \param[in] inliers the data inliers found as supporting the model
137  * \param[in] model_coefficients the initial guess for the model coefficients
138  * \param[out] optimized_coefficients the resultant recomputed coefficients after optimization
139  */
140  void
141  optimizeModelCoefficients (const std::vector<int> &inliers,
142  const Eigen::VectorXf &model_coefficients,
143  Eigen::VectorXf &optimized_coefficients);
144 
145  /** \brief Create a new point cloud with inliers projected onto the line model.
146  * \param[in] inliers the data inliers that we want to project on the line model
147  * \param[in] model_coefficients the *normalized* coefficients of a line model
148  * \param[out] projected_points the resultant projected points
149  * \param[in] copy_data_fields set to true if we need to copy the other data fields
150  */
151  void
152  projectPoints (const std::vector<int> &inliers,
153  const Eigen::VectorXf &model_coefficients,
154  PointCloud &projected_points,
155  bool copy_data_fields = true);
156 
157  /** \brief Verify whether a subset of indices verifies the given line model coefficients.
158  * \param[in] indices the data indices that need to be tested against the line model
159  * \param[in] model_coefficients the line model coefficients
160  * \param[in] threshold a maximum admissible distance threshold for determining the inliers from the outliers
161  */
162  bool
163  doSamplesVerifyModel (const std::set<int> &indices,
164  const Eigen::VectorXf &model_coefficients,
165  const double threshold);
166 
167  /** \brief Return an unique id for this model (SACMODEL_LINE). */
168  inline pcl::SacModel
169  getModelType () const { return (SACMODEL_LINE); }
170 
171  protected:
172  /** \brief Check whether a model is valid given the user constraints.
173  * \param[in] model_coefficients the set of model coefficients
174  */
175  inline bool
176  isModelValid (const Eigen::VectorXf &model_coefficients)
177  {
178  if (model_coefficients.size () != 6)
179  {
180  PCL_ERROR ("[pcl::SampleConsensusModelLine::selectWithinDistance] Invalid number of model coefficients given (%zu)!\n", model_coefficients.size ());
181  return (false);
182  }
183 
184  return (true);
185  }
186 
187  /** \brief Check if a sample of indices results in a good sample of points
188  * indices.
189  * \param[in] samples the resultant index samples
190  */
191  bool
192  isSampleGood (const std::vector<int> &samples) const;
193  };
194 }
195 
196 #ifdef PCL_NO_PRECOMPILE
197 #include <pcl/sample_consensus/impl/sac_model_line.hpp>
198 #endif
199 
200 #endif //#ifndef PCL_SAMPLE_CONSENSUS_MODEL_LINE_H_
pcl::PointCloud< PointT >::Ptr PointCloudPtr
Definition: sac_model.h:71
SampleConsensusModel< PointT >::PointCloud PointCloud
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true)
Create a new point cloud with inliers projected onto the line model.
bool isSampleGood(const std::vector< int > &samples) const
Check if a sample of indices results in a good sample of points indices.
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients)
Check whether the given index samples can form a valid line model, compute the model coefficients fro...
bool isModelValid(const Eigen::VectorXf &model_coefficients)
Check whether a model is valid given the user constraints.
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
Recompute the line coefficients using the given inlier set and return them to the user...
SampleConsensusModelLine(const PointCloudConstPtr &cloud, const std::vector< int > &indices, bool random=false)
Constructor for base SampleConsensusModelLine.
pcl::SacModel getModelType() const
Return an unique id for this model (SACMODEL_LINE).
SampleConsensusModel< PointT >::PointCloudPtr PointCloudPtr
boost::shared_ptr< SampleConsensusModelLine > Ptr
virtual int countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold)
Count all the points which respect the given model coefficients as inliers.
SampleConsensusModel< PointT >::PointCloudConstPtr PointCloudConstPtr
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances)
Compute all squared distances from the cloud data to a given line model.
SacModel
Definition: model_types.h:48
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, std::vector< int > &inliers)
Select all the points which respect the given model coefficients as inliers.
SampleConsensusModel represents the base model class.
Definition: sac_model.h:66
virtual ~SampleConsensusModelLine()
Empty destructor.
A point structure representing Euclidean xyz coordinates, and the RGB color.
pcl::PointCloud< PointT >::ConstPtr PointCloudConstPtr
Definition: sac_model.h:70
SampleConsensusModelLine(const PointCloudConstPtr &cloud, bool random=false)
Constructor for base SampleConsensusModelLine.
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
Verify whether a subset of indices verifies the given line model coefficients.
PointCloud represents the base class in PCL for storing collections of 3D points. ...
SampleConsensusModelLine defines a model for 3D line segmentation.