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
typedef boost::shared_ptr < Filter< PointT > >	Ptr
typedef boost::shared_ptr < const Filter< PointT > >	ConstPtr
typedef PointIndices::Ptr	PointIndicesPtr
typedef PointIndices::ConstPtr	PointIndicesConstPtr
Public Member Functions
	StatisticalOutlierRemoval (bool extract_removed_indices=false)
	Constructor.
void	setMeanK (int nr_k)
	Set the number of nearest neighbors to use for mean distance estimation.
int	getMeanK ()
	Get the number of nearest neighbors to use for mean distance estimation.
void	setStddevMulThresh (double stddev_mult)
	Set the standard deviation multiplier for the distance threshold calculation.
double	getStddevMulThresh ()
	Get the standard deviation multiplier for the distance threshold calculation.
void	filter (PointCloud &output)
	Calls the filtering method and returns the filtered dataset in output.
void	filter (std::vector< int > &indices)
	Calls the filtering method and returns the filtered point cloud indices.
void	setNegative (bool negative)
	Set whether the regular conditions for points filtering should apply, or the inverted conditions.
bool	getNegative ()
	Get whether the regular conditions for points filtering should apply, or the inverted conditions.
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.
bool	getKeepOrganized ()
	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.
void	setUserFilterValue (float value)
	Provide a value that the filtered points should be set to instead of removing them.
IndicesConstPtr const	getRemovedIndices ()
	Get the point indices being removed.
virtual void	setInputCloud (const PointCloudConstPtr &cloud)
	Provide a pointer to the input dataset.
PointCloudConstPtr const	getInputCloud ()
	Get a pointer to the input point cloud dataset.
void	setIndices (const IndicesPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (const IndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (const PointIndicesConstPtr &indices)
	Provide a pointer to the vector of indices that represents the input data.
void	setIndices (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)
	Set the indices for the points laying within an interest region of the point cloud.
IndicesPtr const	getIndices ()
	Get a pointer to the vector of indices used.
const PointT &	operator[] (size_t pos)
	Override PointCloud operator[] to shorten code.

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

Author:: Radu Bogdan Rusu

Definition at line 81 of file statistical_outlier_removal.h.

Member Typedef Documentation

template<typename PointT>

typedef boost::shared_ptr< const Filter<PointT> > pcl::Filter< PointT >::ConstPtr [inherited]

Definition at line 76 of file filter.h.

template<typename PointT>

typedef PointIndices::ConstPtr pcl::PCLBase< PointT >::PointIndicesConstPtr [inherited]

Reimplemented in pcl::EuclideanClusterExtraction< PointT >, pcl::ExtractPolygonalPrismData< PointT >, pcl::SegmentDifferences< PointT >, pcl::LabeledEuclideanClusterExtraction< PointT >, pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >, and pcl::PCA< PointT >.

Definition at line 79 of file pcl_base.h.

template<typename PointT>

typedef PointIndices::Ptr pcl::PCLBase< PointT >::PointIndicesPtr [inherited]

Reimplemented in pcl::EuclideanClusterExtraction< PointT >, pcl::ExtractPolygonalPrismData< PointT >, pcl::SegmentDifferences< PointT >, pcl::LabeledEuclideanClusterExtraction< PointT >, pcl::SampleConsensusInitialAlignment< PointSource, PointTarget, FeatureT >, and pcl::PCA< PointT >.

Definition at line 78 of file pcl_base.h.

template<typename PointT>

typedef boost::shared_ptr< Filter<PointT> > pcl::Filter< PointT >::Ptr [inherited]

Definition at line 75 of file filter.h.

Constructor & Destructor Documentation

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 93 of file statistical_outlier_removal.h.

Member Function Documentation

template<typename PointT>

void pcl::FilterIndices< PointT >::filter ( PointCloud & output ) [inline, inherited]

Calls the filtering method and returns the filtered dataset in output.

Parameters:

[out] output the resultant filtered point cloud dataset

Reimplemented from pcl::Filter< PointT >.

Definition at line 92 of file filter_indices.h.

template<typename PointT>

void pcl::FilterIndices< PointT >::filter ( std::vector< int > & indices ) [inline, inherited]

Calls the filtering method and returns the filtered point cloud indices.

Parameters:

[out] indices the resultant filtered point cloud indices

Definition at line 101 of file filter_indices.h.

template<typename PointT>

IndicesPtr const pcl::PCLBase< PointT >::getIndices ( ) [inline, inherited]

Get a pointer to the vector of indices used.

Definition at line 190 of file pcl_base.h.

template<typename PointT>

PointCloudConstPtr const pcl::PCLBase< PointT >::getInputCloud ( ) [inline, inherited]

Get a pointer to the input point cloud dataset.

Definition at line 107 of file pcl_base.h.

template<typename PointT>

bool pcl::FilterIndices< PointT >::getKeepOrganized ( ) [inline, inherited]

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.

Returns:: The value of the internal keep_organized_ parameter; false = remove points (default), true = redefine points, keep structure.

Definition at line 145 of file filter_indices.h.

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 115 of file statistical_outlier_removal.h.

template<typename PointT>

bool pcl::FilterIndices< PointT >::getNegative ( ) [inline, inherited]

Get whether the regular conditions for points filtering should apply, or the inverted conditions.

Returns:: The value of the internal negative_ parameter; false = normal filter behavior (default), true = inverted behavior.

Definition at line 125 of file filter_indices.h.

template<typename PointT>

IndicesConstPtr const pcl::FilterIndices< PointT >::getRemovedIndices ( ) [inline, inherited]

Get the point indices being removed.

Returns:: The value of the internal negative_ parameter; false = normal filter behavior (default), true = inverted behavior.

Reimplemented from pcl::Filter< PointT >.

Definition at line 164 of file filter_indices.h.

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.

Parameters:

[in] stddev_mult The standard deviation multiplier.

Definition at line 137 of file statistical_outlier_removal.h.

template<typename PointT>

const PointT& pcl::PCLBase< PointT >::operator[] ( size_t pos ) [inline, inherited]

Override PointCloud operator[] to shorten code.

Note:: this method can be called instead of (*input_)[(*indices_)[pos]] or input_->points[(*indices_)[pos]]

Parameters:

pos	position in indices_ vector

Definition at line 197 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const IndicesPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 113 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const IndicesConstPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 124 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices ( const PointIndicesConstPtr & indices ) [inline, inherited]

Provide a pointer to the vector of indices that represents the input data.

Parameters:

indices a pointer to the vector of indices that represents the input data.

Definition at line 135 of file pcl_base.h.

template<typename PointT>

void pcl::PCLBase< PointT >::setIndices	(	size_t	row_start,
		size_t	col_start,
		size_t	nb_rows,
		size_t	nb_cols
	)		`[inline, inherited]`

Set the indices for the points laying within an interest region of the point cloud.

Note:: you shouldn't call this method on unorganized point clouds!

Parameters:

row_start	the offset on rows
col_start	the offset on columns
nb_rows	the number of rows to be considered row_start included
nb_cols	the number of columns to be considered col_start included

Definition at line 151 of file pcl_base.h.

template<typename PointT>

virtual void pcl::PCLBase< PointT >::setInputCloud ( const PointCloudConstPtr & cloud ) [inline, virtual, inherited]

Provide a pointer to the input dataset.

Parameters:

cloud the const boost shared pointer to a PointCloud message

Reimplemented in pcl::PCA< PointT >.

Definition at line 103 of file pcl_base.h.

template<typename PointT>

void pcl::FilterIndices< PointT >::setKeepOrganized ( bool keep_organized ) [inline, inherited]

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.

Parameters:

[in] keep_organized false = remove points (default), true = redefine points, keep structure.

Definition at line 135 of file filter_indices.h.

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 106 of file statistical_outlier_removal.h.

template<typename PointT>

void pcl::FilterIndices< PointT >::setNegative ( bool negative ) [inline, inherited]

Set whether the regular conditions for points filtering should apply, or the inverted conditions.

Parameters:

[in] negative false = normal filter behavior (default), true = inverted behavior.

Definition at line 116 of file filter_indices.h.

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 126 of file statistical_outlier_removal.h.

template<typename PointT>

void pcl::FilterIndices< PointT >::setUserFilterValue ( float value ) [inline, inherited]

Provide a value that the filtered points should be set to instead of removing them.

Used in conjunction with setKeepOrganized ().

Parameters:

[in] value the user given value that the filtered point dimensions should be set to (default = NaN).

Definition at line 155 of file filter_indices.h.

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

/usr/src/RPM/BUILD/PCL-1.6.0-Source/filters/include/pcl/filters/statistical_outlier_removal.h
/usr/src/RPM/BUILD/PCL-1.6.0-Source/filters/include/pcl/filters/impl/statistical_outlier_removal.hpp

Public Types

Public Member Functions

Detailed Description

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

Member Typedef Documentation

Constructor & Destructor Documentation

Member Function Documentation

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