Point Cloud Library (PCL)  1.12.1-dev
statistical_outlier_removal.h
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39
40 #pragma once
41
42 #include <pcl/filters/filter_indices.h>
43 #include <pcl/search/search.h> // for Search
44
45 namespace pcl
46 {
47  /** \brief @b StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
48  * \details The algorithm iterates through the entire input twice:
49  * During the first iteration it will compute the average distance that each point has to its nearest k neighbors.
50  * The value of k can be set using setMeanK().
51  * Next, the mean and standard deviation of all these distances are computed in order to determine a distance threshold.
52  * The distance threshold will be equal to: mean + stddev_mult * stddev.
53  * The multiplier for the standard deviation can be set using setStddevMulThresh().
54  * During the next iteration the points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
55  * <br>
56  * The neighbors found for each query point will be found amongst ALL points of setInputCloud(), not just those indexed by setIndices().
57  * The setIndices() method only indexes the points that will be iterated through as search query points.
58  * <br><br>
60  * - R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz.
61  * Towards 3D Point Cloud Based Object Maps for Household Environments
62  * Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.
63  * <br><br>
64  * Usage example:
65  * \code
66  * pcl::StatisticalOutlierRemoval<PointType> sorfilter (true); // Initializing with true will allow us to extract the removed indices
67  * sorfilter.setInputCloud (cloud_in);
68  * sorfilter.setMeanK (8);
69  * sorfilter.setStddevMulThresh (1.0);
70  * sorfilter.filter (*cloud_out);
71  * // The resulting cloud_out contains all points of cloud_in that have an average distance to their 8 nearest neighbors that is below the computed threshold
72  * // Using a standard deviation multiplier of 1.0 and assuming the average distances are normally distributed there is a 84.1% chance that a point will be an inlier
73  * indices_rem = sorfilter.getRemovedIndices ();
74  * // The indices_rem array indexes all points of cloud_in that are outliers
75  * \endcode
76  * \author Radu Bogdan Rusu
77  * \ingroup filters
78  */
79  template<typename PointT>
81  {
82  protected:
84  using PointCloudPtr = typename PointCloud::Ptr;
87
88  public:
89
90  using Ptr = shared_ptr<StatisticalOutlierRemoval<PointT> >;
91  using ConstPtr = shared_ptr<const StatisticalOutlierRemoval<PointT> >;
92
93
94  /** \brief Constructor.
95  * \param[in] extract_removed_indices Set to true if you want to be able to extract the indices of points being removed (default = false).
96  */
97  StatisticalOutlierRemoval (bool extract_removed_indices = false) :
98  FilterIndices<PointT> (extract_removed_indices),
99  searcher_ (),
100  mean_k_ (1),
101  std_mul_ (0.0)
102  {
103  filter_name_ = "StatisticalOutlierRemoval";
104  }
105
106  /** \brief Set the number of nearest neighbors to use for mean distance estimation.
107  * \param[in] nr_k The number of points to use for mean distance estimation.
108  */
109  inline void
110  setMeanK (int nr_k)
111  {
112  mean_k_ = nr_k;
113  }
114
115  /** \brief Get the number of nearest neighbors to use for mean distance estimation.
116  * \return The number of points to use for mean distance estimation.
117  */
118  inline int
120  {
121  return (mean_k_);
122  }
123
124  /** \brief Set the standard deviation multiplier for the distance threshold calculation.
125  * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
126  * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
127  * \param[in] stddev_mult The standard deviation multiplier.
128  */
129  inline void
130  setStddevMulThresh (double stddev_mult)
131  {
132  std_mul_ = stddev_mult;
133  }
134
135  /** \brief Get the standard deviation multiplier for the distance threshold calculation.
136  * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
137  * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
138  */
139  inline double
141  {
142  return (std_mul_);
143  }
144
145  protected:
155
156  /** \brief Filtered results are indexed by an indices array.
157  * \param[out] indices The resultant indices.
158  */
159  void
160  applyFilter (Indices &indices) override
161  {
162  applyFilterIndices (indices);
163  }
164
165  /** \brief Filtered results are indexed by an indices array.
166  * \param[out] indices The resultant indices.
167  */
168  void
169  applyFilterIndices (Indices &indices);
170
171  private:
172  /** \brief A pointer to the spatial search object. */
173  SearcherPtr searcher_;
174
175  /** \brief The number of points to use for mean distance estimation. */
176  int mean_k_;
177
178  /** \brief Standard deviations threshold (i.e., points outside of
179  * \f$\mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers). */
180  double std_mul_;
181  };
182
183  /** \brief @b StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more
184  * information check:
185  * - R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz.
186  * Towards 3D Point Cloud Based Object Maps for Household Environments
187  * Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.
188  *
189  * \author Radu Bogdan Rusu
190  * \ingroup filters
191  */
192  template<>
194  {
197
200
202  using KdTreePtr = pcl::search::Search<pcl::PointXYZ>::Ptr;
203
207
208  public:
209  /** \brief Empty constructor. */
210  StatisticalOutlierRemoval (bool extract_removed_indices = false) :
211  FilterIndices<pcl::PCLPointCloud2>::FilterIndices (extract_removed_indices), mean_k_ (2),
212  std_mul_ (0.0)
213  {
214  filter_name_ = "StatisticalOutlierRemoval";
215  }
216
217  /** \brief Set the number of points (k) to use for mean distance estimation
218  * \param nr_k the number of points to use for mean distance estimation
219  */
220  inline void
221  setMeanK (int nr_k)
222  {
223  mean_k_ = nr_k;
224  }
225
226  /** \brief Get the number of points to use for mean distance estimation. */
227  inline int
229  {
230  return (mean_k_);
231  }
232
233  /** \brief Set the standard deviation multiplier threshold. All points outside the
234  * \f[ \mu \pm \sigma \cdot std\_mul \f]
235  * will be considered outliers, where \f$\mu \f$ is the estimated mean,
236  * and \f$\sigma \f$ is the standard deviation.
237  * \param std_mul the standard deviation multiplier threshold
238  */
239  inline void
240  setStddevMulThresh (double std_mul)
241  {
242  std_mul_ = std_mul;
243  }
244
245  /** \brief Get the standard deviation multiplier threshold as set by the user. */
246  inline double
248  {
249  return (std_mul_);
250  }
251
252  protected:
253  /** \brief The number of points to use for mean distance estimation. */
254  int mean_k_;
255
256  /** \brief Standard deviations threshold (i.e., points outside of
257  * \f$\mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers).
258  */
259  double std_mul_;
260
261  /** \brief A pointer to the spatial search object. */
262  KdTreePtr tree_;
263
264  void
265  applyFilter (Indices &indices) override;
266
267  void
268  applyFilter (PCLPointCloud2 &output) override;
269
270  /**
271  * \brief Compute the statistical values used in both applyFilter methods.
272  *
273  * This method tries to avoid duplicate code.
274  */
275  virtual void
276  generateStatistics (double& mean, double& variance, double& stddev, std::vector<float>& distances);
277  };
278 }
279
280 #ifdef PCL_NO_PRECOMPILE
281 #include <pcl/filters/impl/statistical_outlier_removal.hpp>
282 #endif
Filter represents the base filter class.
Definition: filter.h:81
shared_ptr< Filter< PointT > > Ptr
Definition: filter.h:83
shared_ptr< const Filter< PointT > > ConstPtr
Definition: filter.h:84
std::string filter_name_
The filter name.
Definition: filter.h:158
FilterIndices represents the base class for filters that are about binary point removal.
PCLPointCloud2::Ptr PCLPointCloud2Ptr
Definition: pcl_base.h:185
PCLPointCloud2::ConstPtr PCLPointCloud2ConstPtr
Definition: pcl_base.h:186
PCL base class.
Definition: pcl_base.h:70
typename PointCloud::Ptr PointCloudPtr
Definition: pcl_base.h:73
typename PointCloud::ConstPtr PointCloudConstPtr
Definition: pcl_base.h:74
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
shared_ptr< PointCloud< PointT > > Ptr
Definition: point_cloud.h:413
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:414
int getMeanK()
Get the number of points to use for mean distance estimation.
void applyFilter(Indices &indices) override
Abstract filter method for point cloud indices.
void applyFilter(PCLPointCloud2 &output) override
Abstract filter method for point cloud.
int mean_k_
The number of points to use for mean distance estimation.
KdTreePtr tree_
A pointer to the spatial search object.
double getStddevMulThresh()
Get the standard deviation multiplier threshold as set by the user.
virtual void generateStatistics(double &mean, double &variance, double &stddev, std::vector< float > &distances)
Compute the statistical values used in both applyFilter methods.
StatisticalOutlierRemoval(bool extract_removed_indices=false)
Empty constructor.
double std_mul_
Standard deviations threshold (i.e., points outside of will be marked as outliers).
void setMeanK(int nr_k)
Set the number of points (k) to use for mean distance estimation.
void setStddevMulThresh(double std_mul)
Set the standard deviation multiplier threshold.
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
StatisticalOutlierRemoval(bool extract_removed_indices=false)
Constructor.
typename pcl::search::Search< PointT >::Ptr SearcherPtr
double getStddevMulThresh()
Get the standard deviation multiplier for the distance threshold calculation.
int getMeanK()
Get the number of nearest neighbors to use for mean distance estimation.
void applyFilter(Indices &indices) override
Filtered results are indexed by an indices array.
void applyFilterIndices(Indices &indices)
Filtered results are indexed by an indices array.
void setStddevMulThresh(double stddev_mult)
Set the standard deviation multiplier for the distance threshold calculation.
void setMeanK(int nr_k)
Set the number of nearest neighbors to use for mean distance estimation.
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition: search.h:81
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
#define PCL_EXPORTS
Definition: pcl_macros.h:323
shared_ptr< ::pcl::PCLPointCloud2 > Ptr
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr
A point structure representing Euclidean xyz coordinates, and the RGB color.