Point Cloud Library (PCL) 1.15.0
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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 * \sa RadiusOutlierRemoval
77 * \author Radu Bogdan Rusu
78 * \ingroup filters
79 */
80 template<typename PointT>
82 {
83 protected:
88
89 public:
90
91 using Ptr = shared_ptr<StatisticalOutlierRemoval<PointT> >;
92 using ConstPtr = shared_ptr<const StatisticalOutlierRemoval<PointT> >;
93
94
95 /** \brief Constructor.
96 * \param[in] extract_removed_indices Set to true if you want to be able to extract the indices of points being removed (default = false).
97 */
98 StatisticalOutlierRemoval (bool extract_removed_indices = false) :
99 FilterIndices<PointT> (extract_removed_indices),
100 searcher_ ()
101 {
102 filter_name_ = "StatisticalOutlierRemoval";
103 }
104
105 /** \brief Set the number of nearest neighbors to use for mean distance estimation.
106 * \param[in] nr_k The number of points to use for mean distance estimation.
107 */
108 inline void
109 setMeanK (int nr_k)
110 {
111 mean_k_ = nr_k;
112 }
113
114 /** \brief Get the number of nearest neighbors to use for mean distance estimation.
115 * \return The number of points to use for mean distance estimation.
116 */
117 inline int
119 {
120 return (mean_k_);
121 }
122
123 /** \brief Set the standard deviation multiplier for the distance threshold calculation.
124 * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
125 * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
126 * \param[in] stddev_mult The standard deviation multiplier.
127 */
128 inline void
129 setStddevMulThresh (double stddev_mult)
130 {
131 std_mul_ = stddev_mult;
132 }
133
134 /** \brief Get the standard deviation multiplier for the distance threshold calculation.
135 * \details The distance threshold will be equal to: mean + stddev_mult * stddev.
136 * Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.
137 */
138 inline double
140 {
141 return (std_mul_);
142 }
143
144 /** \brief Provide a pointer to the search object.
145 * Calling this is optional. If not called, the search method will be chosen automatically.
146 * \param[in] searcher a pointer to the spatial search object.
147 */
148 inline void
149 setSearchMethod (const SearcherPtr &searcher) { searcher_ = searcher; }
150 protected:
151 using PCLBase<PointT>::input_;
160
161 /** \brief Filtered results are indexed by an indices array.
162 * \param[out] indices The resultant indices.
163 */
164 void
165 applyFilter (Indices &indices) override
166 {
167 applyFilterIndices (indices);
168 }
169
170 /** \brief Filtered results are indexed by an indices array.
171 * \param[out] indices The resultant indices.
172 */
173 void
174 applyFilterIndices (Indices &indices);
175
176 private:
177 /** \brief A pointer to the spatial search object. */
178 SearcherPtr searcher_;
179
180 /** \brief The number of points to use for mean distance estimation. */
181 int mean_k_{1};
182
183 /** \brief Standard deviations threshold (i.e., points outside of
184 * \f$ \mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers). */
185 double std_mul_{0.0};
186 };
187
188 /** \brief @b StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more
189 * information check:
190 * - R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz.
191 * Towards 3D Point Cloud Based Object Maps for Household Environments
192 * Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.
193 *
194 * \author Radu Bogdan Rusu
195 * \ingroup filters
196 */
197 template<>
198 class PCL_EXPORTS StatisticalOutlierRemoval<pcl::PCLPointCloud2> : public FilterIndices<pcl::PCLPointCloud2>
199 {
200 using FilterIndices<pcl::PCLPointCloud2>::filter_name_;
201 using FilterIndices<pcl::PCLPointCloud2>::getClassName;
202
203 using FilterIndices<pcl::PCLPointCloud2>::removed_indices_;
204 using FilterIndices<pcl::PCLPointCloud2>::extract_removed_indices_;
205
208
210 using PCLPointCloud2Ptr = PCLPointCloud2::Ptr;
211 using PCLPointCloud2ConstPtr = PCLPointCloud2::ConstPtr;
212
213 public:
214 /** \brief Empty constructor. */
215 StatisticalOutlierRemoval (bool extract_removed_indices = false) :
216 FilterIndices<pcl::PCLPointCloud2>::FilterIndices (extract_removed_indices)
217 {
218 filter_name_ = "StatisticalOutlierRemoval";
219 }
220
221 /** \brief Set the number of points (k) to use for mean distance estimation
222 * \param nr_k the number of points to use for mean distance estimation
223 */
224 inline void
225 setMeanK (int nr_k)
226 {
227 mean_k_ = nr_k;
228 }
229
230 /** \brief Get the number of points to use for mean distance estimation. */
231 inline int
233 {
234 return (mean_k_);
235 }
236
237 /** \brief Set the standard deviation multiplier threshold. All points outside the
238 * \f[ \mu \pm \sigma \cdot std\_mul \f]
239 * will be considered outliers, where \f$ \mu \f$ is the estimated mean,
240 * and \f$ \sigma \f$ is the standard deviation.
241 * \param std_mul the standard deviation multiplier threshold
242 */
243 inline void
244 setStddevMulThresh (double std_mul)
245 {
246 std_mul_ = std_mul;
247 }
248
249 /** \brief Get the standard deviation multiplier threshold as set by the user. */
250 inline double
252 {
253 return (std_mul_);
254 }
255
256 protected:
257 /** \brief The number of points to use for mean distance estimation. */
258 int mean_k_{2};
259
260 /** \brief Standard deviations threshold (i.e., points outside of
261 * \f$ \mu \pm \sigma \cdot std\_mul \f$ will be marked as outliers).
262 */
263 double std_mul_{0.0};
264
265 /** \brief A pointer to the spatial search object. */
266 KdTreePtr tree_;
267
268 void
269 applyFilter (Indices &indices) override;
270
271 void
272 applyFilter (PCLPointCloud2 &output) override;
273
274 /**
275 * \brief Compute the statistical values used in both applyFilter methods.
276 *
277 * This method tries to avoid duplicate code.
278 */
279 virtual void
280 generateStatistics (double& mean, double& variance, double& stddev, std::vector<float>& distances);
281 };
282}
283
284#ifdef PCL_NO_PRECOMPILE
285#include <pcl/filters/impl/statistical_outlier_removal.hpp>
286#endif
Filter represents the base filter class.
Definition filter.h:81
bool extract_removed_indices_
Set to true if we want to return the indices of the removed points.
Definition filter.h:161
const std::string & getClassName() const
Get a string representation of the name of this class.
Definition filter.h:174
std::string filter_name_
The filter name.
Definition filter.h:158
IndicesPtr removed_indices_
Indices of the points that are removed.
Definition filter.h:155
FilterIndices represents the base class for filters that are about binary point removal.
float user_filter_value_
The user given value that the filtered point dimensions should be set to (default = NaN).
bool keep_organized_
False = remove points (default), true = redefine points, keep structure.
bool negative_
False = normal filter behavior (default), true = inverted behavior.
PCL base class.
Definition pcl_base.h:70
PointCloudConstPtr input_
The input point cloud dataset.
Definition pcl_base.h:147
IndicesPtr indices_
A pointer to the vector of point indices to use.
Definition pcl_base.h:150
PointCloud represents the base class in PCL for storing collections of 3D points.
shared_ptr< PointCloud< PointT > > Ptr
shared_ptr< const PointCloud< PointT > > ConstPtr
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.
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.
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 PointCloud::ConstPtr PointCloudConstPtr
typename pcl::search::Search< PointT >::Ptr SearcherPtr
shared_ptr< StatisticalOutlierRemoval< PointT > > Ptr
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.
void setSearchMethod(const SearcherPtr &searcher)
Provide a pointer to the search object.
typename FilterIndices< PointT >::PointCloud PointCloud
shared_ptr< const StatisticalOutlierRemoval< PointT > > ConstPtr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
shared_ptr< ::pcl::PCLPointCloud2 > Ptr
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr
A point structure representing Euclidean xyz coordinates, and the RGB color.