Point Cloud Library (PCL)  1.8.1-dev
covariance_sampling.h
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41 
42 #ifndef PCL_FILTERS_COVARIANCE_SAMPLING_H_
43 #define PCL_FILTERS_COVARIANCE_SAMPLING_H_
44 
45 #include <pcl/filters/filter_indices.h>
46 
47 namespace pcl
48 {
49  /** \brief Point Cloud sampling based on the 6D covariances. It selects the points such that the resulting cloud is
50  * as stable as possible for being registered (against a copy of itself) with ICP. The algorithm adds points to the
51  * resulting cloud incrementally, while trying to keep all the 6 eigenvalues of the covariance matrix as close to each
52  * other as possible.
53  * This class also comes with the \a computeConditionNumber method that returns a number which shows how stable a point
54  * cloud will be when used as input for ICP (the closer the value it is to 1.0, the better).
55  *
56  * Based on the following publication:
57  * * "Geometrically Stable Sampling for the ICP Algorithm" - N. Gelfand, L. Ikemoto, S. Rusinkiewicz, M. Levoy
58  *
59  * \author Alexandru E. Ichim, alex.e.ichim@gmail.com
60  */
61  template <typename PointT, typename PointNT>
62  class CovarianceSampling : public FilterIndices<PointT>
63  {
69 
71  typedef typename Cloud::Ptr CloudPtr;
72  typedef typename Cloud::ConstPtr CloudConstPtr;
73  typedef typename pcl::PointCloud<PointNT>::ConstPtr NormalsConstPtr;
74 
75  public:
76  typedef boost::shared_ptr< CovarianceSampling<PointT, PointNT> > Ptr;
77  typedef boost::shared_ptr< const CovarianceSampling<PointT, PointNT> > ConstPtr;
78 
79  /** \brief Empty constructor. */
81  { filter_name_ = "CovarianceSampling"; }
82 
83  /** \brief Set number of indices to be sampled.
84  * \param[in] samples the number of sample indices
85  */
86  inline void
87  setNumberOfSamples (unsigned int samples)
88  { num_samples_ = samples; }
89 
90  /** \brief Get the value of the internal \a num_samples_ parameter. */
91  inline unsigned int
93  { return (num_samples_); }
94 
95  /** \brief Set the normals computed on the input point cloud
96  * \param[in] normals the normals computed for the input cloud
97  */
98  inline void
99  setNormals (const NormalsConstPtr &normals)
100  { input_normals_ = normals; }
101 
102  /** \brief Get the normals computed on the input point cloud */
103  inline NormalsConstPtr
104  getNormals () const
105  { return (input_normals_); }
106 
107 
108 
109  /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
110  * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
111  * the more stable the cloud is for ICP registration.
112  * \return the condition number
113  */
114  double
116 
117  /** \brief Compute the condition number of the input point cloud. The condition number is the ratio between the
118  * largest and smallest eigenvalues of the 6x6 covariance matrix of the cloud. The closer this number is to 1.0,
119  * the more stable the cloud is for ICP registration.
120  * \param[in] covariance_matrix user given covariance matrix
121  * \return the condition number
122  */
123  static double
124  computeConditionNumber (const Eigen::Matrix<double, 6, 6> &covariance_matrix);
125 
126  /** \brief Computes the covariance matrix of the input cloud.
127  * \param[out] covariance_matrix the computed covariance matrix.
128  * \return whether the computation succeeded or not
129  */
130  bool
131  computeCovarianceMatrix (Eigen::Matrix<double, 6, 6> &covariance_matrix);
132 
133  protected:
134  /** \brief Number of indices that will be returned. */
135  unsigned int num_samples_;
136 
137  /** \brief The normals computed at each point in the input cloud */
138  NormalsConstPtr input_normals_;
139 
140  std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > scaled_points_;
141 
142  bool
143  initCompute ();
144 
145  /** \brief Sample of point indices into a separate PointCloud
146  * \param[out] output the resultant point cloud
147  */
148  void
149  applyFilter (Cloud &output);
150 
151  /** \brief Sample of point indices
152  * \param[out] indices the resultant point cloud indices
153  */
154  void
155  applyFilter (std::vector<int> &indices);
156 
157  static bool
158  sort_dot_list_function (std::pair<int, double> a,
159  std::pair<int, double> b)
160  { return (a.second > b.second); }
161 
162  public:
163  EIGEN_MAKE_ALIGNED_OPERATOR_NEW
164  };
165 }
166 
167 #ifdef PCL_NO_PRECOMPILE
168 #include <pcl/filters/impl/covariance_sampling.hpp>
169 #endif
170 
171 
172 #endif /* PCL_FILTERS_COVARIANCE_SAMPLING_H_ */
unsigned int num_samples_
Number of indices that will be returned.
Point Cloud sampling based on the 6D covariances.
CovarianceSampling()
Empty constructor.
boost::shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:429
void setNormals(const NormalsConstPtr &normals)
Set the normals computed on the input point cloud.
unsigned int getNumberOfSamples() const
Get the value of the internal num_samples_ parameter.
std::vector< Eigen::Vector3f, Eigen::aligned_allocator< Eigen::Vector3f > > scaled_points_
NormalsConstPtr getNormals() const
Get the normals computed on the input point cloud.
boost::shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:428
void setNumberOfSamples(unsigned int samples)
Set number of indices to be sampled.
void applyFilter(Cloud &output)
Sample of point indices into a separate PointCloud.
FilterIndices represents the base class for filters that are about binary point removal.
NormalsConstPtr input_normals_
The normals computed at each point in the input cloud.
static bool sort_dot_list_function(std::pair< int, double > a, std::pair< int, double > b)
std::string filter_name_
The filter name.
Definition: filter.h:166
bool computeCovarianceMatrix(Eigen::Matrix< double, 6, 6 > &covariance_matrix)
Computes the covariance matrix of the input cloud.
double computeConditionNumber()
Compute the condition number of the input point cloud.
boost::shared_ptr< const CovarianceSampling< PointT, PointNT > > ConstPtr
boost::shared_ptr< CovarianceSampling< PointT, PointNT > > Ptr