sac_model_line.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
#include <pcl/sample_consensus/sac_model_line.h>
#include <pcl/common/centroid.h>
#include <pcl/common/concatenate.h>
#include <pcl/common/eigen.h> // for eigen33
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::isSampleGood (const Indices &samples) const
{
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelLine::isSampleGood] Wrong number of samples (is %lu, should be %lu)!\n", samples.size (), sample_size_);
return (false);
}
// Make sure that the two sample points are not identical
if (
((*input_)[samples[0]].x != (*input_)[samples[1]].x)
||
((*input_)[samples[0]].y != (*input_)[samples[1]].y)
||
((*input_)[samples[0]].z != (*input_)[samples[1]].z))
{
return (true);
}
return (false);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::computeModelCoefficients (
const Indices &samples, Eigen::VectorXf &model_coefficients) const
{
// Need 2 samples
if (samples.size () != sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
if (std::abs ((*input_)[samples[0]].x - (*input_)[samples[1]].x) <= std::numeric_limits<float>::epsilon () &&
std::abs ((*input_)[samples[0]].y - (*input_)[samples[1]].y) <= std::numeric_limits<float>::epsilon () &&
std::abs ((*input_)[samples[0]].z - (*input_)[samples[1]].z) <= std::numeric_limits<float>::epsilon ())
{
return (false);
}
model_coefficients.resize (model_size_);
model_coefficients[0] = (*input_)[samples[0]].x;
model_coefficients[1] = (*input_)[samples[0]].y;
model_coefficients[2] = (*input_)[samples[0]].z;
model_coefficients[3] = (*input_)[samples[1]].x - model_coefficients[0];
model_coefficients[4] = (*input_)[samples[1]].y - model_coefficients[1];
model_coefficients[5] = (*input_)[samples[1]].z - model_coefficients[2];
model_coefficients.template tail<3> ().normalize ();
PCL_DEBUG ("[pcl::SampleConsensusModelLine::computeModelCoefficients] Model is (%g,%g,%g,%g,%g,%g).\n",
model_coefficients[0], model_coefficients[1], model_coefficients[2],
model_coefficients[3], model_coefficients[4], model_coefficients[5]);
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
return;
}
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 (std::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)
// Need to estimate sqrt here to keep MSAC and friends general
distances[i] = sqrt ((line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ());
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers)
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return;
double sqr_threshold = threshold * threshold;
inliers.clear ();
error_sqr_dists_.clear ();
inliers.reserve (indices_->size ());
error_sqr_dists_.reserve (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 (std::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)
double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
{
// Returns the indices of the points whose squared distances are smaller than the threshold
inliers.push_back ((*indices_)[i]);
error_sqr_dists_.push_back (sqr_distance);
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> std::size_t
pcl::SampleConsensusModelLine<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
return (0);
double sqr_threshold = threshold * threshold;
std::size_t nr_p = 0;
// Obtain the line point and direction
Eigen::Vector4f line_pt (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
line_dir.normalize ();
// Iterate through the 3d points and calculate the distances from them to the line
for (std::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)
double sqr_distance = (line_pt - (*input_)[(*indices_)[i]].getVector4fMap ()).cross3 (line_dir).squaredNorm ();
if (sqr_distance < sqr_threshold)
nr_p++;
}
return (nr_p);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::optimizeModelCoefficients (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const
{
// Needs a valid set of model coefficients
if (!isModelValid (model_coefficients))
{
optimized_coefficients = model_coefficients;
return;
}
// Need more than the minimum sample size to make a difference
if (inliers.size () <= sample_size_)
{
PCL_ERROR ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] Not enough inliers to refine/optimize the model's coefficients (%lu)! Returning the same coefficients.\n", inliers.size ());
optimized_coefficients = model_coefficients;
return;
}
optimized_coefficients.resize (model_size_);
// Compute the 3x3 covariance matrix
Eigen::Vector4f centroid;
if (0 == compute3DCentroid (*input_, inliers, centroid))
{
PCL_WARN ("[pcl::SampleConsensusModelLine::optimizeModelCoefficients] compute3DCentroid failed (returned 0) because there are no valid inliers.\n");
optimized_coefficients = model_coefficients;
return;
}
Eigen::Matrix3f covariance_matrix;
computeCovarianceMatrix (*input_, inliers, centroid, covariance_matrix);
optimized_coefficients[0] = centroid[0];
optimized_coefficients[1] = centroid[1];
optimized_coefficients[2] = centroid[2];
// Extract the eigenvalues and eigenvectors
EIGEN_ALIGN16 Eigen::Vector3f eigen_values;
EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_values);
pcl::computeCorrespondingEigenVector (covariance_matrix, eigen_values [2], eigen_vector);
//pcl::eigen33 (covariance_matrix, eigen_vectors, eigen_values);
optimized_coefficients.template tail<3> ().matrix () = eigen_vector;
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelLine<PointT>::projectPoints (
const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields) const
{
// 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], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
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.resize (input_->size ());
projected_points.width = input_->width;
projected_points.height = input_->height;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < projected_points.size (); ++i)
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[i], projected_points[i]));
// Iterate through the 3d points and calculate the distances from them to the line
for (const auto &inlier : inliers)
{
Eigen::Vector4f pt ((*input_)[inlier].x, (*input_)[inlier].y, (*input_)[inlier].z, 0.0f);
// 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[inlier].x = pp[0];
projected_points[inlier].y = pp[1];
projected_points[inlier].z = pp[2];
}
}
else
{
// Allocate enough space and copy the basics
projected_points.resize (inliers.size ());
projected_points.width = inliers.size ();
projected_points.height = 1;
using FieldList = typename pcl::traits::fieldList<PointT>::type;
// Iterate over each point
for (std::size_t i = 0; i < inliers.size (); ++i)
// Iterate over each dimension
pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> ((*input_)[inliers[i]], projected_points[i]));
// Iterate through the 3d points and calculate the distances from them to the line
for (std::size_t i = 0; i < inliers.size (); ++i)
{
Eigen::Vector4f pt ((*input_)[inliers[i]].x, (*input_)[inliers[i]].y, (*input_)[inliers[i]].z, 0.0f);
// 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[i].x = pp[0];
projected_points[i].y = pp[1];
projected_points[i].z = pp[2];
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelLine<PointT>::doSamplesVerifyModel (
const std::set<index_t> &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const
{
// 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], 0.0f);
Eigen::Vector4f line_dir (model_coefficients[3], model_coefficients[4], model_coefficients[5], 0.0f);
line_dir.normalize ();
double sqr_threshold = threshold * threshold;
// Iterate through the 3d points and calculate the distances from them to the line
for (const auto &index : indices)
{
// 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_)[index].getVector4fMap ()).cross3 (line_dir).squaredNorm () > sqr_threshold)
return (false);
}
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelLine(T) template class PCL_EXPORTS pcl::SampleConsensusModelLine<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_LINE_H_
centroid.h
Define methods for centroid estimation and covariance matrix calculus.
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition: point_cloud.h:173
pcl::PointCloud::is_dense
bool is_dense
True if no points are invalid (e.g., have NaN or Inf values in any of their floating point fields).
Definition: point_cloud.h:403
pcl::PointCloud::resize
void resize(std::size_t count)
Resizes the container to contain count elements.
Definition: point_cloud.h:462
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:398
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition: point_cloud.h:392
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:400
pcl::PointCloud::size
std::size_t size() const
Definition: point_cloud.h:443
pcl::SampleConsensusModelLine::computeModelCoefficients
bool computeModelCoefficients(const Indices &samples, Eigen::VectorXf &model_coefficients) const override
Check whether the given index samples can form a valid line model, compute the model coefficients fro...
Definition: sac_model_line.hpp:74
pcl::SampleConsensusModelLine::countWithinDistance
std::size_t countWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold) const override
Count all the points which respect the given model coefficients as inliers.
Definition: sac_model_line.hpp:174
pcl::SampleConsensusModelLine::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances) const override
Compute all squared distances from the cloud data to a given line model.
Definition: sac_model_line.hpp:109
pcl::SampleConsensusModelLine::isSampleGood
bool isSampleGood(const Indices &samples) const override
Check if a sample of indices results in a good sample of points indices.
Definition: sac_model_line.hpp:51
pcl::SampleConsensusModelLine::doSamplesVerifyModel
bool doSamplesVerifyModel(const std::set< index_t > &indices, const Eigen::VectorXf &model_coefficients, const double threshold) const override
Verify whether a subset of indices verifies the given line model coefficients.
Definition: sac_model_line.hpp:324
pcl::SampleConsensusModelLine::optimizeModelCoefficients
void optimizeModelCoefficients(const Indices &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients) const override
Recompute the line coefficients using the given inlier set and return them to the user.
Definition: sac_model_line.hpp:205
pcl::SampleConsensusModelLine::projectPoints
void projectPoints(const Indices &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields=true) const override
Create a new point cloud with inliers projected onto the line model.
Definition: sac_model_line.hpp:251
pcl::SampleConsensusModelLine::selectWithinDistance
void selectWithinDistance(const Eigen::VectorXf &model_coefficients, const double threshold, Indices &inliers) override
Select all the points which respect the given model coefficients as inliers.
Definition: sac_model_line.hpp:137
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:226
pcl::computeCovarianceMatrix
unsigned int computeCovarianceMatrix(const pcl::PointCloud< PointT > &cloud, const Eigen::Matrix< Scalar, 4, 1 > &centroid, Eigen::Matrix< Scalar, 3, 3 > &covariance_matrix)
Compute the 3x3 covariance matrix of a given set of points.
Definition: centroid.hpp:180
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:296
pcl::compute3DCentroid
unsigned int compute3DCentroid(ConstCloudIterator< PointT > &cloud_iterator, Eigen::Matrix< Scalar, 4, 1 > &centroid)
Compute the 3D (X-Y-Z) centroid of a set of points and return it as a 3D vector.
Definition: centroid.hpp:56
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition: types.h:133
pcl::NdConcatenateFunctor
Helper functor structure for concatenate.
Definition: concatenate.h:50