Point Cloud Library (PCL) 1.12.0
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
45namespace 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>
59 * For more information:
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:
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
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. */
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.