template <typename PointT> inline unsigned int
pcl::computeCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
const std::vector<int> &indices,
Eigen::Matrix3d &covariance_matrix)
{
// create the buffer on the stack which is much faster than using cloud.points[indices[i]] and centroid as a buffer
Eigen::Matrix<double, 1, 6, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 6, Eigen::RowMajor>::Zero ();
unsigned int point_count;
if (cloud.is_dense)
{
point_count = indices.size ();
for (std::vector<int>::const_iterator iIt = indices.begin (); iIt != indices.end (); ++iIt)
{
//const PointT& point = cloud[*iIt];
accu [0] += cloud[*iIt].x * cloud[*iIt].x;
accu [1] += cloud[*iIt].x * cloud[*iIt].y;
accu [2] += cloud[*iIt].x * cloud[*iIt].z;
accu [3] += cloud[*iIt].y * cloud[*iIt].y;
accu [4] += cloud[*iIt].y * cloud[*iIt].z;
accu [5] += cloud[*iIt].z * cloud[*iIt].z;
}
}
else
{
point_count = 0;
for (std::vector<int>::const_iterator iIt = indices.begin (); iIt != indices.end (); ++iIt)
{
if (!isFinite (cloud[*iIt]))
continue;
++point_count;
accu [0] += cloud[*iIt].x * cloud[*iIt].x;
accu [1] += cloud[*iIt].x * cloud[*iIt].y;
accu [2] += cloud[*iIt].x * cloud[*iIt].z;
accu [3] += cloud[*iIt].y * cloud[*iIt].y;
accu [4] += cloud[*iIt].y * cloud[*iIt].z;
accu [5] += cloud[*iIt].z * cloud[*iIt].z;
}
}
if (point_count != 0)
{
accu /= static_cast<double> (point_count);
covariance_matrix.coeffRef (0) = accu [0];
covariance_matrix.coeffRef (1) = covariance_matrix.coeffRef (3) = accu [1];
covariance_matrix.coeffRef (2) = covariance_matrix.coeffRef (6) = accu [2];
covariance_matrix.coeffRef (4) = accu [3];
covariance_matrix.coeffRef (5) = covariance_matrix.coeffRef (7) = accu [4];
covariance_matrix.coeffRef (8) = accu [5];
}
return (point_count);
}
template <typename PointT> inline unsigned int
pcl::computeCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
Eigen::Matrix3d &covariance_matrix)
{
// create the buffer on the stack which is much faster than using cloud.points[indices[i]] and centroid as a buffer
Eigen::Matrix<double, 1, 6, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 6, Eigen::RowMajor>::Zero ();
unsigned int point_count;
if (cloud.is_dense)
{
point_count = cloud.size ();
// For each point in the cloud
for (size_t i = 0; i < point_count; ++i)
{
accu [0] += cloud[i].x * cloud[i].x;
accu [1] += cloud[i].x * cloud[i].y;
accu [2] += cloud[i].x * cloud[i].z;
accu [3] += cloud[i].y * cloud[i].y;
accu [4] += cloud[i].y * cloud[i].z;
accu [5] += cloud[i].z * cloud[i].z;
}
}
else
{
point_count = 0;
for (size_t i = 0; i < cloud.points.size (); ++i)
{
if (!isFinite (cloud[i]))
continue;
accu [0] += cloud[i].x * cloud[i].x;
accu [1] += cloud[i].x * cloud[i].y;
accu [2] += cloud[i].x * cloud[i].z;
accu [3] += cloud[i].y * cloud[i].y;
accu [4] += cloud[i].y * cloud[i].z;
accu [5] += cloud[i].z * cloud[i].z;
++point_count;
}
}
if (point_count != 0)
{
accu /= static_cast<double> (point_count);
covariance_matrix.coeffRef (0) = accu [0];
covariance_matrix.coeffRef (1) = covariance_matrix.coeffRef (3) = accu [1];
covariance_matrix.coeffRef (2) = covariance_matrix.coeffRef (6) = accu [2];
covariance_matrix.coeffRef (4) = accu [3];
covariance_matrix.coeffRef (5) = covariance_matrix.coeffRef (7) = accu [4];
covariance_matrix.coeffRef (8) = accu [5];
}
return (point_count);
}
void rpyToRotationMatrix(const Eigen::Vector3d& rpy, Eigen::Matrix3d& mat)
{
double sin_a = sin(rpy.coeff(2));
double cos_a = cos(rpy.coeff(2));
double sin_b = sin(rpy.coeff(1));
double cos_b = cos(rpy.coeff(1));
double sin_g = sin(rpy.coeff(0));
double cos_g = cos(rpy.coeff(0));
mat.coeffRef(0, 0) = cos_a * cos_b;
mat.coeffRef(0, 1) = cos_a * sin_b * sin_g - sin_a * cos_g;
mat.coeffRef(0, 2) = cos_a * sin_b * cos_g + sin_a * sin_g;
mat.coeffRef(1, 0) = sin_a * cos_b;
mat.coeffRef(1, 1) = sin_a * sin_b * sin_g + cos_a * cos_g;
mat.coeffRef(1, 2) = sin_a * sin_b * cos_g - cos_a * sin_g;
mat.coeffRef(2, 0) = -sin_b;
mat.coeffRef(2, 1) = cos_b * sin_g;
mat.coeffRef(2, 2) = cos_b * cos_g;
}
void rpyToRotationMatrix(const std::vector<double>& rpy, Eigen::Matrix3d& mat)
{
double sin_a = sin(rpy[2]);
double cos_a = cos(rpy[2]);
double sin_b = sin(rpy[1]);
double cos_b = cos(rpy[1]);
double sin_g = sin(rpy[0]);
double cos_g = cos(rpy[0]);
mat.coeffRef(0, 0) = cos_a * cos_b;
mat.coeffRef(0, 1) = cos_a * sin_b * sin_g - sin_a * cos_g;
mat.coeffRef(0, 2) = cos_a * sin_b * cos_g + sin_a * sin_g;
mat.coeffRef(1, 0) = sin_a * cos_b;
mat.coeffRef(1, 1) = sin_a * sin_b * sin_g + cos_a * cos_g;
mat.coeffRef(1, 2) = sin_a * sin_b * cos_g - cos_a * sin_g;
mat.coeffRef(2, 0) = -sin_b;
mat.coeffRef(2, 1) = cos_b * sin_g;
mat.coeffRef(2, 2) = cos_b * cos_g;
}
template <typename PointT> inline unsigned int
pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
const std::vector<int> &indices,
Eigen::Matrix3d &covariance_matrix,
Eigen::Vector4d ¢roid)
{
// create the buffer on the stack which is much faster than using cloud.points[indices[i]] and centroid as a buffer
Eigen::Matrix<double, 1, 9, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 9, Eigen::RowMajor>::Zero ();
unsigned point_count;
if (cloud.is_dense)
{
point_count = indices.size ();
for (std::vector<int>::const_iterator iIt = indices.begin (); iIt != indices.end (); ++iIt)
{
//const PointT& point = cloud[*iIt];
accu [0] += cloud[*iIt].x * cloud[*iIt].x;
accu [1] += cloud[*iIt].x * cloud[*iIt].y;
accu [2] += cloud[*iIt].x * cloud[*iIt].z;
accu [3] += cloud[*iIt].y * cloud[*iIt].y;
accu [4] += cloud[*iIt].y * cloud[*iIt].z;
accu [5] += cloud[*iIt].z * cloud[*iIt].z;
accu [6] += cloud[*iIt].x;
accu [7] += cloud[*iIt].y;
accu [8] += cloud[*iIt].z;
}
}
else
{
point_count = 0;
for (std::vector<int>::const_iterator iIt = indices.begin (); iIt != indices.end (); ++iIt)
{
if (!isFinite (cloud[*iIt]))
continue;
++point_count;
accu [0] += cloud[*iIt].x * cloud[*iIt].x;
accu [1] += cloud[*iIt].x * cloud[*iIt].y;
accu [2] += cloud[*iIt].x * cloud[*iIt].z;
accu [3] += cloud[*iIt].y * cloud[*iIt].y; // 4
accu [4] += cloud[*iIt].y * cloud[*iIt].z; // 5
accu [5] += cloud[*iIt].z * cloud[*iIt].z; // 8
accu [6] += cloud[*iIt].x;
accu [7] += cloud[*iIt].y;
accu [8] += cloud[*iIt].z;
}
}
if (point_count != 0)
{
accu /= (double) point_count;
Eigen::Vector3f vec = accu.tail<3> ();
centroid.head<3> () = vec;//= accu.tail<3> ();
centroid[3] = 0;
covariance_matrix.coeffRef (0) = accu [0] - accu [6] * accu [6];
covariance_matrix.coeffRef (1) = accu [1] - accu [6] * accu [7];
covariance_matrix.coeffRef (2) = accu [2] - accu [6] * accu [8];
covariance_matrix.coeffRef (4) = accu [3] - accu [7] * accu [7];
covariance_matrix.coeffRef (5) = accu [4] - accu [7] * accu [8];
covariance_matrix.coeffRef (8) = accu [5] - accu [8] * accu [8];
covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);
covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);
covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);
}
return (point_count);
}
template <typename PointT> inline unsigned int
pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
Eigen::Matrix3d &covariance_matrix,
Eigen::Vector4d ¢roid)
{
// create the buffer on the stack which is much faster than using cloud.points[indices[i]] and centroid as a buffer
Eigen::Matrix<double, 1, 9, Eigen::RowMajor> accu = Eigen::Matrix<double, 1, 9, Eigen::RowMajor>::Zero ();
unsigned int point_count;
if (cloud.is_dense)
{
point_count = cloud.size ();
// For each point in the cloud
for (size_t i = 0; i < point_count; ++i)
{
accu [0] += cloud[i].x * cloud[i].x;
accu [1] += cloud[i].x * cloud[i].y;
accu [2] += cloud[i].x * cloud[i].z;
accu [3] += cloud[i].y * cloud[i].y;
accu [4] += cloud[i].y * cloud[i].z;
accu [5] += cloud[i].z * cloud[i].z;
accu [6] += cloud[i].x;
accu [7] += cloud[i].y;
accu [8] += cloud[i].z;
}
}
else
{
point_count = 0;
for (size_t i = 0; i < cloud.points.size (); ++i)
{
if (!isFinite (cloud[i]))
continue;
accu [0] += cloud[i].x * cloud[i].x;
accu [1] += cloud[i].x * cloud[i].y;
accu [2] += cloud[i].x * cloud[i].z;
accu [3] += cloud[i].y * cloud[i].y;
accu [4] += cloud[i].y * cloud[i].z;
accu [5] += cloud[i].z * cloud[i].z;
accu [6] += cloud[i].x;
accu [7] += cloud[i].y;
accu [8] += cloud[i].z;
++point_count;
}
}
if (point_count != 0)
{
accu /= (double) point_count;
centroid.head<3> () = accu.tail<3> ();
centroid[3] = 0;
covariance_matrix.coeffRef (0) = accu [0] - accu [6] * accu [6];
covariance_matrix.coeffRef (1) = accu [1] - accu [6] * accu [7];
covariance_matrix.coeffRef (2) = accu [2] - accu [6] * accu [8];
covariance_matrix.coeffRef (4) = accu [3] - accu [7] * accu [7];
covariance_matrix.coeffRef (5) = accu [4] - accu [7] * accu [8];
covariance_matrix.coeffRef (8) = accu [5] - accu [8] * accu [8];
covariance_matrix.coeffRef (3) = covariance_matrix.coeff (1);
covariance_matrix.coeffRef (6) = covariance_matrix.coeff (2);
covariance_matrix.coeffRef (7) = covariance_matrix.coeff (5);
}
return (point_count);
}
