sac_model_plane.hpp
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#ifndef PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
#define PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
#include <pcl/sample_consensus/sac_model_plane.h>
#include <pcl/common/centroid.h>
#include <pcl/common/eigen.h>
#include <pcl/common/concatenate.h>
#include <pcl/point_types.h>
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::isSampleGood (const std::vector<int> &samples) const
{
// Need an extra check in case the sample selection is empty
if (samples.empty ())
return (false);
// Get the values at the two points
pcl::Array4fMapConst p0 = input_->points[samples[0]].getArray4fMap ();
pcl::Array4fMapConst p1 = input_->points[samples[1]].getArray4fMap ();
pcl::Array4fMapConst p2 = input_->points[samples[2]].getArray4fMap ();
Eigen::Array4f dy1dy2 = (p1-p0) / (p2-p0);
return ( (dy1dy2[0] != dy1dy2[1]) || (dy1dy2[2] != dy1dy2[1]) );
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::computeModelCoefficients (
const std::vector<int> &samples, Eigen::VectorXf &model_coefficients)
{
// Need 3 samples
if (samples.size () != 3)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::computeModelCoefficients] Invalid set of samples given (%lu)!\n", samples.size ());
return (false);
}
pcl::Array4fMapConst p0 = input_->points[samples[0]].getArray4fMap ();
pcl::Array4fMapConst p1 = input_->points[samples[1]].getArray4fMap ();
pcl::Array4fMapConst p2 = input_->points[samples[2]].getArray4fMap ();
// Compute the segment values (in 3d) between p1 and p0
Eigen::Array4f p1p0 = p1 - p0;
// Compute the segment values (in 3d) between p2 and p0
Eigen::Array4f p2p0 = p2 - p0;
// Avoid some crashes by checking for collinearity here
Eigen::Array4f dy1dy2 = p1p0 / p2p0;
if ( (dy1dy2[0] == dy1dy2[1]) && (dy1dy2[2] == dy1dy2[1]) ) // Check for collinearity
return (false);
// Compute the plane coefficients from the 3 given points in a straightforward manner
// calculate the plane normal n = (p2-p1) x (p3-p1) = cross (p2-p1, p3-p1)
model_coefficients.resize (4);
model_coefficients[0] = p1p0[1] * p2p0[2] - p1p0[2] * p2p0[1];
model_coefficients[1] = p1p0[2] * p2p0[0] - p1p0[0] * p2p0[2];
model_coefficients[2] = p1p0[0] * p2p0[1] - p1p0[1] * p2p0[0];
model_coefficients[3] = 0;
// Normalize
model_coefficients.normalize ();
// ... + d = 0
model_coefficients[3] = -1 * (model_coefficients.template head<4>().dot (p0.matrix ()));
return (true);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::getDistancesToModel (
const Eigen::VectorXf &model_coefficients, std::vector<double> &distances)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::getDistancesToModel] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return;
}
distances.resize (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the plane
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
/*distances[i] = fabs (model_coefficients[0] * input_->points[(*indices_)[i]].x +
model_coefficients[1] * input_->points[(*indices_)[i]].y +
model_coefficients[2] * input_->points[(*indices_)[i]].z +
model_coefficients[3]);*/
Eigen::Vector4f pt (input_->points[(*indices_)[i]].x,
input_->points[(*indices_)[i]].y,
input_->points[(*indices_)[i]].z,
1);
distances[i] = fabs (model_coefficients.dot (pt));
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::selectWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold, std::vector<int> &inliers)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::selectWithinDistance] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return;
}
int nr_p = 0;
inliers.resize (indices_->size ());
error_sqr_dists_.resize (indices_->size ());
// Iterate through the 3d points and calculate the distances from them to the plane
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f pt (input_->points[(*indices_)[i]].x,
input_->points[(*indices_)[i]].y,
input_->points[(*indices_)[i]].z,
1);
float distance = fabsf (model_coefficients.dot (pt));
if (distance < threshold)
{
// Returns the indices of the points whose distances are smaller than the threshold
inliers[nr_p] = (*indices_)[i];
error_sqr_dists_[nr_p] = static_cast<double> (distance);
++nr_p;
}
}
inliers.resize (nr_p);
error_sqr_dists_.resize (nr_p);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> int
pcl::SampleConsensusModelPlane<PointT>::countWithinDistance (
const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::countWithinDistance] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return (0);
}
int nr_p = 0;
// Iterate through the 3d points and calculate the distances from them to the plane
for (size_t i = 0; i < indices_->size (); ++i)
{
// Calculate the distance from the point to the plane normal as the dot product
// D = (P-A).N/|N|
Eigen::Vector4f pt (input_->points[(*indices_)[i]].x,
input_->points[(*indices_)[i]].y,
input_->points[(*indices_)[i]].z,
1);
if (fabs (model_coefficients.dot (pt)) < threshold)
nr_p++;
}
return (nr_p);
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::optimizeModelCoefficients (
const std::vector<int> &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
optimized_coefficients = model_coefficients;
return;
}
// Need at least 3 points to estimate a plane
if (inliers.size () < 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::optimizeModelCoefficients] Not enough inliers found to support a model (%lu)! Returning the same coefficients.\n", inliers.size ());
optimized_coefficients = model_coefficients;
return;
}
Eigen::Vector4f plane_parameters;
// Use Least-Squares to fit the plane through all the given sample points and find out its coefficients
EIGEN_ALIGN16 Eigen::Matrix3f covariance_matrix;
Eigen::Vector4f xyz_centroid;
computeMeanAndCovarianceMatrix (*input_, inliers, covariance_matrix, xyz_centroid);
// Compute the model coefficients
EIGEN_ALIGN16 Eigen::Vector3f::Scalar eigen_value;
EIGEN_ALIGN16 Eigen::Vector3f eigen_vector;
pcl::eigen33 (covariance_matrix, eigen_value, eigen_vector);
// Hessian form (D = nc . p_plane (centroid here) + p)
optimized_coefficients.resize (4);
optimized_coefficients[0] = eigen_vector [0];
optimized_coefficients[1] = eigen_vector [1];
optimized_coefficients[2] = eigen_vector [2];
optimized_coefficients[3] = 0;
optimized_coefficients[3] = -1 * optimized_coefficients.dot (xyz_centroid);
// Make sure it results in a valid model
if (!isModelValid (optimized_coefficients))
{
optimized_coefficients = model_coefficients;
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> void
pcl::SampleConsensusModelPlane<PointT>::projectPoints (
const std::vector<int> &inliers, const Eigen::VectorXf &model_coefficients, PointCloud &projected_points, bool copy_data_fields)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::projectPoints] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return;
}
projected_points.header = input_->header;
projected_points.is_dense = input_->is_dense;
Eigen::Vector4f mc (model_coefficients[0], model_coefficients[1], model_coefficients[2], 0);
// normalize the vector perpendicular to the plane...
mc.normalize ();
// ... and store the resulting normal as a local copy of the model coefficients
Eigen::Vector4f tmp_mc = model_coefficients;
tmp_mc[0] = mc[0];
tmp_mc[1] = mc[1];
tmp_mc[2] = mc[2];
// 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 < input_->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 plane
for (size_t i = 0; i < inliers.size (); ++i)
{
// Calculate the distance from the point to the plane
Eigen::Vector4f p (input_->points[inliers[i]].x,
input_->points[inliers[i]].y,
input_->points[inliers[i]].z,
1);
// use normalized coefficients to calculate the scalar projection
float distance_to_plane = tmp_mc.dot (p);
pcl::Vector4fMap pp = projected_points.points[inliers[i]].getVector4fMap ();
pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
}
}
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 plane
for (size_t i = 0; i < inliers.size (); ++i)
{
// Calculate the distance from the point to the plane
Eigen::Vector4f p (input_->points[inliers[i]].x,
input_->points[inliers[i]].y,
input_->points[inliers[i]].z,
1);
// use normalized coefficients to calculate the scalar projection
float distance_to_plane = tmp_mc.dot (p);
pcl::Vector4fMap pp = projected_points.points[i].getVector4fMap ();
pp.matrix () = p - mc * distance_to_plane; // mc[3] = 0, therefore the 3rd coordinate is safe
}
}
}
//////////////////////////////////////////////////////////////////////////
template <typename PointT> bool
pcl::SampleConsensusModelPlane<PointT>::doSamplesVerifyModel (
const std::set<int> &indices, const Eigen::VectorXf &model_coefficients, const double threshold)
{
// Needs a valid set of model coefficients
if (model_coefficients.size () != 4)
{
PCL_ERROR ("[pcl::SampleConsensusModelPlane::doSamplesVerifyModel] Invalid number of model coefficients given (%lu)!\n", model_coefficients.size ());
return (false);
}
for (std::set<int>::const_iterator it = indices.begin (); it != indices.end (); ++it)
{
Eigen::Vector4f pt (input_->points[*it].x,
input_->points[*it].y,
input_->points[*it].z,
1);
if (fabs (model_coefficients.dot (pt)) > threshold)
return (false);
}
return (true);
}
#define PCL_INSTANTIATE_SampleConsensusModelPlane(T) template class PCL_EXPORTS pcl::SampleConsensusModelPlane<T>;
#endif // PCL_SAMPLE_CONSENSUS_IMPL_SAC_MODEL_PLANE_H_
pcl::Array4fMapConst
const Eigen::Map< const Eigen::Array4f, Eigen::Aligned > Array4fMapConst
Definition: point_types.hpp:149
pcl::PointCloud::points
std::vector< PointT, Eigen::aligned_allocator< PointT > > points
The point data.
Definition: point_cloud.h:410
pcl::EIGEN_ALIGN16
struct pcl::_PointXYZHSV EIGEN_ALIGN16
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::SampleConsensusModelPlane::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_plane.hpp:143
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::SampleConsensusModelPlane::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 plane model.
Definition: sac_model_plane.hpp:261
pcl::SampleConsensusModelPlane::computeModelCoefficients
bool computeModelCoefficients(const std::vector< int > &samples, Eigen::VectorXf &model_coefficients)
Check whether the given index samples can form a valid plane model, compute the model coefficients fr...
Definition: sac_model_plane.hpp:69
pcl::traits::fieldList
Definition: point_traits.h:176
pcl::SampleConsensusModelPlane::getDistancesToModel
void getDistancesToModel(const Eigen::VectorXf &model_coefficients, std::vector< double > &distances)
Compute all distances from the cloud data to a given plane model.
Definition: sac_model_plane.hpp:112
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::SampleConsensusModelPlane::optimizeModelCoefficients
void optimizeModelCoefficients(const std::vector< int > &inliers, const Eigen::VectorXf &model_coefficients, Eigen::VectorXf &optimized_coefficients)
Recompute the plane coefficients using the given inlier set and return them to the user...
Definition: sac_model_plane.hpp:212
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::Vector4fMap
Eigen::Map< Eigen::Vector4f, Eigen::Aligned > Vector4fMap
Definition: point_types.hpp:152
pcl::NdConcatenateFunctor
Helper functor structure for concatenate.
Definition: concatenate.h:64
pcl::SampleConsensusModelPlane
SampleConsensusModelPlane defines a model for 3D plane segmentation.
Definition: sac_model_plane.h:136
pcl::SampleConsensusModelPlane::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 plane model coefficients.
Definition: sac_model_plane.hpp:345
pcl::SampleConsensusModelPlane::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_plane.hpp:183