projection_matrix.hpp
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#ifndef __PCL_ORGANIZED_PROJECTION_MATRIX_HPP__
#define __PCL_ORGANIZED_PROJECTION_MATRIX_HPP__
#include <pcl/cloud_iterator.h>
///////////////////////////////////////////////////////////////////////////////////////////
namespace pcl
{
namespace common
{
namespace internal
{
template <typename MatrixT> void
makeSymmetric (MatrixT& matrix, bool use_upper_triangular = true)
{
if (use_upper_triangular && (MatrixT::Flags & Eigen::RowMajorBit))
{
matrix.coeffRef (4) = matrix.coeff (1);
matrix.coeffRef (8) = matrix.coeff (2);
matrix.coeffRef (9) = matrix.coeff (6);
matrix.coeffRef (12) = matrix.coeff (3);
matrix.coeffRef (13) = matrix.coeff (7);
matrix.coeffRef (14) = matrix.coeff (11);
}
else
{
matrix.coeffRef (1) = matrix.coeff (4);
matrix.coeffRef (2) = matrix.coeff (8);
matrix.coeffRef (6) = matrix.coeff (9);
matrix.coeffRef (3) = matrix.coeff (12);
matrix.coeffRef (7) = matrix.coeff (13);
matrix.coeffRef (11) = matrix.coeff (14);
}
}
}
}
}
//////////////////////////////////////////////////////////////////////////////
template <typename PointT> double
pcl::estimateProjectionMatrix (
typename pcl::PointCloud<PointT>::ConstPtr cloud,
Eigen::Matrix<float, 3, 4, Eigen::RowMajor>& projection_matrix,
const std::vector<int>& indices)
{
// internally we calculate with double but store the result into float matrices.
using Scalar = double;
projection_matrix.setZero ();
if (cloud->height == 1 || cloud->width == 1)
{
PCL_ERROR ("[pcl::estimateProjectionMatrix] Input dataset is not organized!\n");
return (-1.0);
}
Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor> A = Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor>::Zero ();
Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor> B = Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor>::Zero ();
Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor> C = Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor>::Zero ();
Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor> D = Eigen::Matrix<Scalar, 4, 4, Eigen::RowMajor>::Zero ();
pcl::ConstCloudIterator <PointT> pointIt (*cloud, indices);
while (pointIt)
{
unsigned yIdx = pointIt.getCurrentPointIndex () / cloud->width;
unsigned xIdx = pointIt.getCurrentPointIndex () % cloud->width;
const PointT& point = *pointIt;
if (std::isfinite (point.x))
{
Scalar xx = point.x * point.x;
Scalar xy = point.x * point.y;
Scalar xz = point.x * point.z;
Scalar yy = point.y * point.y;
Scalar yz = point.y * point.z;
Scalar zz = point.z * point.z;
Scalar xx_yy = xIdx * xIdx + yIdx * yIdx;
A.coeffRef (0) += xx;
A.coeffRef (1) += xy;
A.coeffRef (2) += xz;
A.coeffRef (3) += point.x;
A.coeffRef (5) += yy;
A.coeffRef (6) += yz;
A.coeffRef (7) += point.y;
A.coeffRef (10) += zz;
A.coeffRef (11) += point.z;
A.coeffRef (15) += 1.0;
B.coeffRef (0) -= xx * xIdx;
B.coeffRef (1) -= xy * xIdx;
B.coeffRef (2) -= xz * xIdx;
B.coeffRef (3) -= point.x * static_cast<double>(xIdx);
B.coeffRef (5) -= yy * xIdx;
B.coeffRef (6) -= yz * xIdx;
B.coeffRef (7) -= point.y * static_cast<double>(xIdx);
B.coeffRef (10) -= zz * xIdx;
B.coeffRef (11) -= point.z * static_cast<double>(xIdx);
B.coeffRef (15) -= xIdx;
C.coeffRef (0) -= xx * yIdx;
C.coeffRef (1) -= xy * yIdx;
C.coeffRef (2) -= xz * yIdx;
C.coeffRef (3) -= point.x * static_cast<double>(yIdx);
C.coeffRef (5) -= yy * yIdx;
C.coeffRef (6) -= yz * yIdx;
C.coeffRef (7) -= point.y * static_cast<double>(yIdx);
C.coeffRef (10) -= zz * yIdx;
C.coeffRef (11) -= point.z * static_cast<double>(yIdx);
C.coeffRef (15) -= yIdx;
D.coeffRef (0) += xx * xx_yy;
D.coeffRef (1) += xy * xx_yy;
D.coeffRef (2) += xz * xx_yy;
D.coeffRef (3) += point.x * xx_yy;
D.coeffRef (5) += yy * xx_yy;
D.coeffRef (6) += yz * xx_yy;
D.coeffRef (7) += point.y * xx_yy;
D.coeffRef (10) += zz * xx_yy;
D.coeffRef (11) += point.z * xx_yy;
D.coeffRef (15) += xx_yy;
}
++pointIt;
} // while
pcl::common::internal::makeSymmetric (A);
pcl::common::internal::makeSymmetric (B);
pcl::common::internal::makeSymmetric (C);
pcl::common::internal::makeSymmetric (D);
Eigen::Matrix<Scalar, 12, 12, Eigen::RowMajor> X = Eigen::Matrix<Scalar, 12, 12, Eigen::RowMajor>::Zero ();
X.topLeftCorner<4,4> ().matrix () = A;
X.block<4,4> (0, 8).matrix () = B;
X.block<4,4> (8, 0).matrix () = B;
X.block<4,4> (4, 4).matrix () = A;
X.block<4,4> (4, 8).matrix () = C;
X.block<4,4> (8, 4).matrix () = C;
X.block<4,4> (8, 8).matrix () = D;
Eigen::SelfAdjointEigenSolver<Eigen::Matrix<Scalar, 12, 12, Eigen::RowMajor> > ei_symm (X);
Eigen::Matrix<Scalar, 12, 12, Eigen::RowMajor> eigen_vectors = ei_symm.eigenvectors ();
// check whether the residual MSE is low. If its high, the cloud was not captured from a projective device.
Eigen::Matrix<Scalar, 1, 1> residual_sqr = eigen_vectors.col (0).transpose () * X * eigen_vectors.col (0);
double residual = residual_sqr.coeff (0);
projection_matrix.coeffRef (0) = static_cast <float> (eigen_vectors.coeff (0));
projection_matrix.coeffRef (1) = static_cast <float> (eigen_vectors.coeff (12));
projection_matrix.coeffRef (2) = static_cast <float> (eigen_vectors.coeff (24));
projection_matrix.coeffRef (3) = static_cast <float> (eigen_vectors.coeff (36));
projection_matrix.coeffRef (4) = static_cast <float> (eigen_vectors.coeff (48));
projection_matrix.coeffRef (5) = static_cast <float> (eigen_vectors.coeff (60));
projection_matrix.coeffRef (6) = static_cast <float> (eigen_vectors.coeff (72));
projection_matrix.coeffRef (7) = static_cast <float> (eigen_vectors.coeff (84));
projection_matrix.coeffRef (8) = static_cast <float> (eigen_vectors.coeff (96));
projection_matrix.coeffRef (9) = static_cast <float> (eigen_vectors.coeff (108));
projection_matrix.coeffRef (10) = static_cast <float> (eigen_vectors.coeff (120));
projection_matrix.coeffRef (11) = static_cast <float> (eigen_vectors.coeff (132));
if (projection_matrix.coeff (0) < 0)
projection_matrix *= -1.0;
return (residual);
}
#endif
pcl
This file defines compatibility wrappers for low level I/O functions.
Definition: convolution.h:45
pcl::PointCloud::height
std::uint32_t height
The point cloud height (if organized as an image-structure).
Definition: point_cloud.h:402
pcl::ConstCloudIterator::getCurrentPointIndex
unsigned getCurrentPointIndex() const
Definition: cloud_iterator.hpp:523
pcl::PointXYZRGB
A point structure representing Euclidean xyz coordinates, and the RGB color.
Definition: point_types.hpp:623
pcl::PointCloud::width
std::uint32_t width
The point cloud width (if organized as an image-structure).
Definition: point_cloud.h:400
pcl::common::internal::makeSymmetric
void makeSymmetric(MatrixT &matrix, bool use_upper_triangular=true)
Definition: projection_matrix.hpp:51
pcl::estimateProjectionMatrix
double estimateProjectionMatrix(typename pcl::PointCloud< PointT >::ConstPtr cloud, Eigen::Matrix< float, 3, 4, Eigen::RowMajor > &projection_matrix, const std::vector< int > &indices=std::vector< int >())
Estimates the projection matrix P = K * (R|-R*t) from organized point clouds, with K = [[fx,...
Definition: projection_matrix.hpp:78
pcl::B
Definition: norms.h:54
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition: point_cloud.h:416
pcl::ConstCloudIterator
Iterator class for point clouds with or without given indices.
Definition: cloud_iterator.h:120