Point Cloud Library (PCL)
1.11.1
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42 #include <pcl/filters/filter_indices.h>
43 #include <pcl/search/pcl_search.h>
79 template<
typename Po
intT>
90 using Ptr = shared_ptr<StatisticalOutlierRemoval<PointT> >;
91 using ConstPtr = shared_ptr<const StatisticalOutlierRemoval<PointT> >;
132 std_mul_ = stddev_mult;
215 filter_name_ =
"StatisticalOutlierRemoval";
281 #ifdef PCL_NO_PRECOMPILE
282 #include <pcl/filters/impl/statistical_outlier_removal.hpp>
shared_ptr< Filter< PointT > > Ptr
typename PointCloud::ConstPtr PointCloudConstPtr
typename PointCloud::Ptr PointCloudPtr
shared_ptr< ::pcl::PCLPointCloud2 > Ptr
void setStddevMulThresh(double std_mul)
Set the standard deviation multiplier threshold.
void setStddevMulThresh(double stddev_mult)
Set the standard deviation multiplier for the distance threshold calculation.
typename pcl::search::Search< PointT >::Ptr SearcherPtr
double getStddevMulThresh()
Get the standard deviation multiplier threshold as set by the user.
int getMeanK()
Get the number of nearest neighbors to use for mean distance estimation.
PCLPointCloud2::ConstPtr PCLPointCloud2ConstPtr
PointCloud represents the base class in PCL for storing collections of 3D points.
void setMeanK(int nr_k)
Set the number of nearest neighbors to use for mean distance estimation.
void applyFilterIndices(std::vector< int > &indices)
Filtered results are indexed by an indices array.
A point structure representing Euclidean xyz coordinates, and the RGB color.
double getStddevMulThresh()
Get the standard deviation multiplier for the distance threshold calculation.
virtual void generateStatistics(double &mean, double &variance, double &stddev, std::vector< float > &distances)
Compute the statistical values used in both applyFilter methods.
PCLPointCloud2::Ptr PCLPointCloud2Ptr
shared_ptr< const ::pcl::PCLPointCloud2 > ConstPtr
StatisticalOutlierRemoval(bool extract_removed_indices=false)
Constructor.
void applyFilter(std::vector< int > &indices) override
Abstract filter method for point cloud indices.
double std_mul_
Standard deviations threshold (i.e., points outside of will be marked as outliers).
StatisticalOutlierRemoval(bool extract_removed_indices=false)
Empty constructor.
shared_ptr< const Filter< PointT > > ConstPtr
shared_ptr< pcl::search::Search< PointT > > Ptr
FilterIndices represents the base class for filters that are about binary point removal.
Filter represents the base filter class.
std::string filter_name_
The filter name.
shared_ptr< PointCloud< PointT > > Ptr
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
int getMeanK()
Get the number of points to use for mean distance estimation.
KdTreePtr tree_
A pointer to the spatial search object.
shared_ptr< const PointCloud< PointT > > ConstPtr
int mean_k_
The number of points to use for mean distance estimation.
void applyFilter(std::vector< int > &indices) override
Filtered results are indexed by an indices array.
void applyFilter(PCLPointCloud2 &output) override
Abstract filter method for point cloud.
void setMeanK(int nr_k)
Set the number of points (k) to use for mean distance estimation.