StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. More...

#include <pcl/filters/statistical_outlier_removal.h>

Inheritance diagram for pcl::StatisticalOutlierRemoval< PointT >:

Collaboration diagram for pcl::StatisticalOutlierRemoval< PointT >:

Public Types
using	Ptr = shared_ptr< StatisticalOutlierRemoval< PointT > >

using	ConstPtr = shared_ptr< const StatisticalOutlierRemoval< PointT > >

Public Types inherited from pcl::FilterIndices< PointT >
using	PointCloud = pcl::PointCloud< PointT >

using	Ptr = shared_ptr< FilterIndices< PointT > >

using	ConstPtr = shared_ptr< const FilterIndices< PointT > >

Public Types inherited from pcl::Filter< PointT >
using	Ptr = shared_ptr< Filter< PointT > >

using	ConstPtr = shared_ptr< const Filter< PointT > >

using	PointCloud = pcl::PointCloud< PointT >

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

Public Types inherited from pcl::PCLBase< PointT >
using	PointCloud = pcl::PointCloud< PointT >

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

using	PointIndicesPtr = PointIndices::Ptr

using	PointIndicesConstPtr = PointIndices::ConstPtr

Public Member Functions
	StatisticalOutlierRemoval (bool extract_removed_indices=false)
	Constructor. More...

void	setMeanK (int nr_k)
	Set the number of nearest neighbors to use for mean distance estimation. More...

int	getMeanK ()
	Get the number of nearest neighbors to use for mean distance estimation. More...

void	setStddevMulThresh (double stddev_mult)
	Set the standard deviation multiplier for the distance threshold calculation. More...

double	getStddevMulThresh ()
	Get the standard deviation multiplier for the distance threshold calculation. More...

void	setSearchMethod (const SearcherPtr &searcher)
	Provide a pointer to the search object. More...

Public Member Functions inherited from pcl::FilterIndices< PointT >
	FilterIndices (bool extract_removed_indices=false)
	Constructor. More...

void	filter (Indices &indices)
	Calls the filtering method and returns the filtered point cloud indices. More...

void	setNegative (bool negative)
	Set whether the regular conditions for points filtering should apply, or the inverted conditions. More...

bool	getNegative () const
	Get whether the regular conditions for points filtering should apply, or the inverted conditions. More...

void	setKeepOrganized (bool keep_organized)
	Set whether the filtered points should be kept and set to the value given through setUserFilterValue (default: NaN), or removed from the PointCloud, thus potentially breaking its organized structure. More...

bool	getKeepOrganized () const
	Get whether the filtered points should be kept and set to the value given through setUserFilterValue (default = NaN), or removed from the PointCloud, thus potentially breaking its organized structure. More...

void	setUserFilterValue (float value)
	Provide a value that the filtered points should be set to instead of removing them. More...

Public Member Functions inherited from pcl::Filter< PointT >
	Filter (bool extract_removed_indices=false)
	Empty constructor. More...

IndicesConstPtr const	getRemovedIndices () const
	Get the point indices being removed. More...

void	getRemovedIndices (PointIndices &pi)
	Get the point indices being removed. More...

void	filter (PointCloud &output)
	Calls the filtering method and returns the filtered dataset in output. More...

Public Member Functions inherited from pcl::PCLBase< PointT >
	PCLBase ()
	Empty constructor. More...

	PCLBase (const PCLBase &base)
	Copy constructor. More...

virtual	~PCLBase ()=default
	Destructor. More...

virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset. More...

PointCloudConstPtr const	getInputCloud () const
	Get a pointer to the input point cloud dataset. More...

virtual void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data. More...

virtual void	setIndices (std::size_t row_start, std::size_t col_start, std::size_t nb_rows, std::size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud. More...

IndicesPtr	getIndices ()
	Get a pointer to the vector of indices used. More...

IndicesConstPtr const	getIndices () const
	Get a pointer to the vector of indices used. More...

const PointT &	operator[] (std::size_t pos) const
	Override PointCloud operator[] to shorten code. More...

Protected Types
using	PointCloud = typename FilterIndices< PointT >::PointCloud

using	PointCloudPtr = typename PointCloud::Ptr

using	PointCloudConstPtr = typename PointCloud::ConstPtr

using	SearcherPtr = typename pcl::search::Search< PointT >::Ptr

Protected Member Functions
void	applyFilter (Indices &indices) override
	Filtered results are indexed by an indices array. More...

void	applyFilterIndices (Indices &indices)
	Filtered results are indexed by an indices array. More...

Protected Member Functions inherited from pcl::FilterIndices< PointT >
void	applyFilter (PointCloud &output) override
	Abstract filter method for point cloud. More...

Protected Member Functions inherited from pcl::Filter< PointT >
const std::string &	getClassName () const
	Get a string representation of the name of this class. More...

Protected Member Functions inherited from pcl::PCLBase< PointT >
bool	initCompute ()
	This method should get called before starting the actual computation. More...

bool	deinitCompute ()
	This method should get called after finishing the actual computation. More...

Additional Inherited Members
Protected Attributes inherited from pcl::FilterIndices< PointT >
bool	negative_ {false}
	False = normal filter behavior (default), true = inverted behavior. More...

bool	keep_organized_ {false}
	False = remove points (default), true = redefine points, keep structure. More...

float	user_filter_value_
	The user given value that the filtered point dimensions should be set to (default = NaN). More...

Protected Attributes inherited from pcl::Filter< PointT >
IndicesPtr	removed_indices_
	Indices of the points that are removed. More...

std::string	filter_name_
	The filter name. More...

bool	extract_removed_indices_
	Set to true if we want to return the indices of the removed points. More...

Protected Attributes inherited from pcl::PCLBase< PointT >
PointCloudConstPtr	input_
	The input point cloud dataset. More...

IndicesPtr	indices_
	A pointer to the vector of point indices to use. More...

bool	use_indices_
	Set to true if point indices are used. More...

bool	fake_indices_
	If no set of indices are given, we construct a set of fake indices that mimic the input PointCloud. More...

Detailed Description

template<typename PointT>
class pcl::StatisticalOutlierRemoval< PointT >

StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.

The algorithm iterates through the entire input twice: During the first iteration it will compute the average distance that each point has to its nearest k neighbors. The value of k can be set using setMeanK(). Next, the mean and standard deviation of all these distances are computed in order to determine a distance threshold. The distance threshold will be equal to: mean + stddev_mult * stddev. The multiplier for the standard deviation can be set using setStddevMulThresh(). 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.
The neighbors found for each query point will be found amongst ALL points of setInputCloud(), not just those indexed by setIndices(). The setIndices() method only indexes the points that will be iterated through as search query points.

For more information:

R. B. Rusu, Z. C. Marton, N. Blodow, M. Dolha, and M. Beetz. Towards 3D Point Cloud Based Object Maps for Household Environments Robotics and Autonomous Systems Journal (Special Issue on Semantic Knowledge), 2008.

Usage example:
pcl::StatisticalOutlierRemoval<PointType> sorfilter (true); // Initializing with true will allow us to extract the removed indices

sorfilter.setInputCloud (cloud_in);

sorfilter.setMeanK (8);

sorfilter.setStddevMulThresh (1.0);

sorfilter.filter (*cloud_out);

// 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

// 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

indices_rem = sorfilter.getRemovedIndices ();

// The indices_rem array indexes all points of cloud_in that are outliers

pcl::StatisticalOutlierRemoval
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
Definition: statistical_outlier_removal.h:81

Author
Radu Bogdan Rusu

Definition at line 80 of file statistical_outlier_removal.h.

Member Typedef Documentation

◆ ConstPtr

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::ConstPtr = shared_ptr<const StatisticalOutlierRemoval<PointT> >

Definition at line 91 of file statistical_outlier_removal.h.

◆ PointCloud

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::PointCloud = typename FilterIndices<PointT>::PointCloud

protected

Definition at line 83 of file statistical_outlier_removal.h.

◆ PointCloudConstPtr

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::PointCloudConstPtr = typename PointCloud::ConstPtr

protected

Definition at line 85 of file statistical_outlier_removal.h.

◆ PointCloudPtr

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::PointCloudPtr = typename PointCloud::Ptr

protected

Definition at line 84 of file statistical_outlier_removal.h.

◆ Ptr

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::Ptr = shared_ptr<StatisticalOutlierRemoval<PointT> >

Definition at line 90 of file statistical_outlier_removal.h.

◆ SearcherPtr

template<typename PointT >

using pcl::StatisticalOutlierRemoval< PointT >::SearcherPtr = typename pcl::search::Search<PointT>::Ptr

protected

Definition at line 86 of file statistical_outlier_removal.h.

Constructor & Destructor Documentation

◆ StatisticalOutlierRemoval()

template<typename PointT >

pcl::StatisticalOutlierRemoval< PointT >::StatisticalOutlierRemoval ( bool extract_removed_indices = false )

inline

Constructor.

Parameters

[in] extract_removed_indices Set to true if you want to be able to extract the indices of points being removed (default = false).

Definition at line 97 of file statistical_outlier_removal.h.

References pcl::Filter< PointT >::filter_name_.

Member Function Documentation

◆ applyFilter()

template<typename PointT >

void pcl::StatisticalOutlierRemoval< PointT >::applyFilter ( Indices & indices )

inlineoverrideprotectedvirtual

Filtered results are indexed by an indices array.

Parameters

[out] indices The resultant indices.

Implements pcl::FilterIndices< PointT >.

Definition at line 164 of file statistical_outlier_removal.h.

References pcl::StatisticalOutlierRemoval< PointT >::applyFilterIndices().

◆ applyFilterIndices()

template<typename PointT >

void pcl::StatisticalOutlierRemoval< PointT >::applyFilterIndices ( Indices & indices )

protected

Filtered results are indexed by an indices array.

Parameters

[out] indices The resultant indices.

Definition at line 49 of file statistical_outlier_removal.hpp.

References pcl::geometry::distance().

Referenced by pcl::StatisticalOutlierRemoval< PointT >::applyFilter().

◆ getMeanK()

template<typename PointT >

int pcl::StatisticalOutlierRemoval< PointT >::getMeanK ( )

inline

Get the number of nearest neighbors to use for mean distance estimation.

Returns: The number of points to use for mean distance estimation.

Definition at line 117 of file statistical_outlier_removal.h.

◆ getStddevMulThresh()

template<typename PointT >

double pcl::StatisticalOutlierRemoval< PointT >::getStddevMulThresh ( )

inline

Get the standard deviation multiplier for the distance threshold calculation.

The distance threshold will be equal to: mean + stddev_mult * stddev. Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.

Definition at line 138 of file statistical_outlier_removal.h.

◆ setMeanK()

template<typename PointT >

void pcl::StatisticalOutlierRemoval< PointT >::setMeanK ( int nr_k )

inline

Set the number of nearest neighbors to use for mean distance estimation.

Parameters

[in] nr_k The number of points to use for mean distance estimation.

Definition at line 108 of file statistical_outlier_removal.h.

◆ setSearchMethod()

template<typename PointT >

void pcl::StatisticalOutlierRemoval< PointT >::setSearchMethod ( const SearcherPtr & searcher )

inline

Provide a pointer to the search object.

Calling this is optional. If not called, the search method will be chosen automatically.

Parameters

[in] searcher a pointer to the spatial search object.

Definition at line 148 of file statistical_outlier_removal.h.

◆ setStddevMulThresh()

template<typename PointT >

void pcl::StatisticalOutlierRemoval< PointT >::setStddevMulThresh ( double stddev_mult )

inline

Set the standard deviation multiplier for the distance threshold calculation.

The distance threshold will be equal to: mean + stddev_mult * stddev. Points will be classified as inlier or outlier if their average neighbor distance is below or above this threshold respectively.

Parameters

[in] stddev_mult The standard deviation multiplier.

Definition at line 128 of file statistical_outlier_removal.h.

The documentation for this class was generated from the following files:

pcl/filters/statistical_outlier_removal.h
pcl/filters/impl/statistical_outlier_removal.hpp

Public Types

Public Member Functions

Protected Types

Protected Member Functions

Additional Inherited Members

Detailed Description

template<typename PointT> class pcl::StatisticalOutlierRemoval< PointT >

Member Typedef Documentation

◆ ConstPtr

◆ PointCloud

◆ PointCloudConstPtr

◆ PointCloudPtr

◆ Ptr

◆ SearcherPtr

Constructor & Destructor Documentation

◆ StatisticalOutlierRemoval()

Member Function Documentation

◆ applyFilter()

◆ applyFilterIndices()

◆ getMeanK()

◆ getStddevMulThresh()

◆ setMeanK()

◆ setSearchMethod()

◆ setStddevMulThresh()

template<typename PointT>
class pcl::StatisticalOutlierRemoval< PointT >