sac_model_stick.hpp
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* $Id: sac_model_line.hpp 2328 2011-08-31 08:11:00Z rusu $
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
#include <pcl/sample_consensus/sac_model_stick.h>
#include <pcl/common/centroid.h>
#include <pcl/common/concatenate.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::isSampleGood (const std::vector<int> &samples) const
{
if (
(input_->points[samples[0]].x != input_->points[samples[1]].x)
&&
(input_->points[samples[0]].y != input_->points[samples[1]].y)
&&
(input_->points[samples[0]].z != input_->points[samples[1]].z))
return (true);
return (false);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::computeModelCoefficients (
const std::vector<int> &samples, Eigen::VectorXf &model_coefficients)
{
// Need 2 samples
if (samples.size () != 2)
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
model_coefficients.resize (7);
model_coefficients[0] = input_->points[samples[0]].x;
model_coefficients[1] = input_->points[samples[0]].y;
model_coefficients[2] = input_->points[samples[0]].z;
model_coefficients[3] = input_->points[samples[1]].x;
model_coefficients[4] = input_->points[samples[1]].y;
model_coefficients[5] = input_->points[samples[1]].z;
// model_coefficients[3] = input_->points[samples[1]].x - model_coefficients[0];
// model_coefficients[4] = input_->points[samples[1]].y - model_coefficients[1];
// model_coefficients[5] = input_->points[samples[1]].z - model_coefficients[2];
// model_coefficients.template segment<3> (3).normalize ();
// We don't care about model_coefficients[6] which is the width (radius) of the stick
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return;
float sqr_threshold = static_cast<float> (radius_max_ * radius_max_);
distances.resize (indices_->size ());
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
line_dir.normalize ();
// Iterate through the 3d points and calculate the distances from them to the line
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
float sqr_distance = (line_pt - input_->points[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
// Need to estimate sqrt here to keep MSAC and friends general
distances[i] = sqrt (sqr_distance);
else
// Penalize outliers by doubling the distance
distances[i] = 2 * sqrt (sqr_distance);
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, std::vector<int> &inliers)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return;
float sqr_threshold = static_cast<float> (threshold * threshold);
int nr_p = 0;
inliers.resize (indices_->size ());
error_sqr_dists_.resize (indices_->size ());
// Obtain the line point and direction
Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
Eigen::Vector4f line_dir = line_pt2 - line_pt1;
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
//Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
line_dir.normalize ();
//float norm = line_dir.squaredNorm ();
// Iterate through the 3d points and calculate the distances from them to the line
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
Eigen::Vector4f dir = input_->points[(*indices_)[i]].getVector4fMap () - line_pt1;
//float u = dir.dot (line_dir);
// If the point falls outside of the segment, ignore it
//if (u < 0.0f || u > 1.0f)
// continue;
float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
{
// Returns the indices of the points whose squared distances are smaller than the threshold
inliers[nr_p] = (*indices_)[i];
error_sqr_dists_[nr_p] = static_cast<double> (sqr_distance);
++nr_p;
}
}
inliers.resize (nr_p);
error_sqr_dists_.resize (nr_p);
}
///////////////////////////////////////////////////////////////////////////
template <typename PointT> int
pcl::SampleConsensusModelStick<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return (0);
float sqr_threshold = static_cast<float> (threshold * threshold);
int nr_i = 0, nr_o = 0;
// Obtain the line point and direction
Eigen::Vector4f line_pt1 (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
Eigen::Vector4f line_pt2 (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
Eigen::Vector4f line_dir = line_pt2 - line_pt1;
line_dir.normalize ();
//Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
// Iterate through the 3d points and calculate the distances from them to the line
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
Eigen::Vector4f dir = input_->points[(*indices_)[i]].getVector4fMap () - line_pt1;
//float u = dir.dot (line_dir);
// If the point falls outside of the segment, ignore it
//if (u < 0.0f || u > 1.0f)
// continue;
float sqr_distance = dir.cross3 (line_dir).squaredNorm ();
// Use a larger threshold (4 times the radius) to get more points in
if (sqr_distance < sqr_threshold)
nr_i++;
else if (sqr_distance < 4 * sqr_threshold)
nr_o++;
}
return (nr_i - nr_o < 0 ? 0 : nr_i - nr_o);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::optimizeModelCoefficients (
const std::vector<int> &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
optimized_coefficients = model_coefficients;
return;
}
// Need at least 2 points to estimate a line
if (inliers.size () <= 2)
{
PCL_ERROR ("[pcl::SampleConsensusModelStick::optimizeModelCoefficients] Not enough inliers found to support a model (%lu)! Returning the same coefficients.\n", inliers.size ());
optimized_coefficients = model_coefficients;
return;
}
optimized_coefficients.resize (7);
// Compute the 3x3 covariance matrix
Eigen::Vector4f centroid;
Eigen::Matrix3f covariance_matrix;
computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, centroid);
optimized_coefficients[0] = centroid[0];
optimized_coefficients[1] = centroid[1];
optimized_coefficients[2] = centroid[2];
// Extract the eigenvalues and eigenvectors
Eigen::Vector3f eigen_values;
Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_values);
pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
optimized_coefficients.template segment<3> (3).matrix () = eigen_vector;
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelStick<PointT>::projectPoints (
const std::vector<int> &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields)
{
// Needs a valid model coefficients
if (!isModelValid (model_coefficients))
return;
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
projected_points.header = input_->header;
projected_points.is_dense = input_->is_dense;
// Copy all the data fields from the input cloud to the projected one?
if (copy_data_fields)
{
// Allocate enough space and copy the basics
projected_points.points.resize (input_->points.size ());
projected_points.width = input_->width;
projected_points.height = input_->height;
typedef typename pcl::traits::fieldList<PointT>::type FieldList;
// Iterate over each point
for (size_t i = 0; i < projected_points.points.size (); ++i)
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> (input_->points[i], projected_points.points[i]));
// Iterate through the 3d points and calculate the distances from them to the line
for (size_t i = 0; i < inliers.size (); ++i)
{
Eigen::Vector4f pt (input_->points[inliers[i]].x, input_->points[inliers[i]].y, input_->points[inliers[i]].z, 0);
// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
Eigen::Vector4f pp = line_pt + k * line_dir;
// Calculate the projection of the point on the line (pointProj = A + k * B)
projected_points.points[inliers[i]].x = pp[0];
projected_points.points[inliers[i]].y = pp[1];
projected_points.points[inliers[i]].z = pp[2];
}
}
else
{
// Allocate enough space and copy the basics
projected_points.points.resize (inliers.size ());
projected_points.width = static_cast<uint32_t> (inliers.size ());
projected_points.height = 1;
typedef typename pcl::traits::fieldList<PointT>::type FieldList;
// Iterate over each point
for (size_t i = 0; i < inliers.size (); ++i)
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> (input_->points[inliers[i]], projected_points.points[i]));
// Iterate through the 3d points and calculate the distances from them to the line
for (size_t i = 0; i < inliers.size (); ++i)
{
Eigen::Vector4f pt (input_->points[inliers[i]].x, input_->points[inliers[i]].y, input_->points[inliers[i]].z, 0);
// double k = (DOT_PROD_3D (points[i], p21) - dotA_B) / dotB_B;
float k = (pt.dot (line_dir) - line_pt.dot (line_dir)) / line_dir.dot (line_dir);
Eigen::Vector4f pp = line_pt + k * line_dir;
// Calculate the projection of the point on the line (pointProj = A + k * B)
projected_points.points[i].x = pp[0];
projected_points.points[i].y = pp[1];
projected_points.points[i].z = pp[2];
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelStick<PointT>::doSamplesVerifyModel (
const std::set<int> &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return (false);
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
Eigen::Vector4f line_dir (model_coefficients[3] - model_coefficients[0], model_coefficients[4] - model_coefficients[1], model_coefficients[5] - model_coefficients[2], 0);
//Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0);
line_dir.normalize ();
float sqr_threshold = static_cast<float> (threshold * threshold);
// Iterate through the 3d points and calculate the distances from them to the line
for (std::set<int>::const_iterator it = indices.begin (); it != indices.end (); ++it)
{
// Calculate the distance from the point to the line
// D = ||(P2-P1) x (P1-P0)|| / ||P2-P1|| = norm (cross (p2-p1, p2-p0)) / norm(p2-p1)
if ((line_pt - input_->points[*it].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
return (false);
}
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelStick(T) template class PCL_EXPORTS pcl::SampleConsensusModelStick<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_STICK_H_
pcl::computeCorrespondingEigenVector
void computeCorrespondingEigenVector(const Matrix &mat, const typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the corresponding eigenvector to the given eigenvalue of the symmetric positive semi defin...
Definition: eigen.hpp:219
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::computeMeanAndCovarianceMatrix
unsigned int computeMeanAndCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the normalized 3x3 covariance matrix and the centroid of a given set of points in a single lo...
Definition: centroid.hpp:490
pcl::SampleConsensusModelStick::optimizeModelCoefficients
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
Recompute the stick coefficients using the given inlier set and return them to the user...
Definition: sac_model_stick.hpp:223
pcl::SampleConsensusModelStick::computeModelCoefficients
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients)
Check whether the given index samples can form a valid stick model, compute the model coefficients fr...
Definition: sac_model_stick.hpp:65
pcl::SampleConsensusModelStick::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< int > &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
Verify whether a subset of indices verifies the given stick model coefficients.
Definition: sac_model_stick.hpp:337
pcl::PointCloud::height
uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:415
pcl::PointCloud::width
uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:413
pcl::traits::fieldList
Definition: point_traits.h:176
pcl::SampleConsensusModelStick::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances)
Compute all squared distances from the cloud data to a given stick model.
Definition: sac_model_stick.hpp:96
pcl::SampleConsensusModelStick::isSampleGood
bool isSampleGood(const std::vector< int > &samples) const
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_stick.hpp:50
pcl::SampleConsensusModelStick::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, std::vector< int > &inliers)
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_stick.hpp:129
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points. ...
Definition: projection_matrix.h:53
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:407
pcl::eigen33
void eigen33(const Matrix &mat, typename Matrix::Scalar &eigenvalue, Vector &eigenvector)
determines the eigenvector and eigenvalue of the smallest eigenvalue of the symmetric positive semi d...
Definition: eigen.hpp:251
pcl::SampleConsensusModelStick::countWithinDistance
virtual int countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold)
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_stick.hpp:178
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values).
Definition: point_cloud.h:418
pcl::SampleConsensusModelStick::projectPoints
void projectPoints(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true)
Create a new point cloud with inliers projected onto the stick model.
Definition: sac_model_stick.hpp:264
pcl::NdConcatenateFunctor
Helper functor structure for concatenate.
Definition: concatenate.h:64