本文整理汇总了C++中eigen::Matrix3d::coeff方法的典型用法代码示例。如果您正苦于以下问题：C++ Matrix3d::coeff方法的具体用法？C++ Matrix3d::coeff怎么用？C++ Matrix3d::coeff使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类eigen::Matrix3d的用法示例。
在下文中一共展示了Matrix3d::coeff方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C++代码示例。
示例1: getEuler
/*
* Decomposes the orientation in euler angles so that this can be
* obtained by applying the following rotations in order:
*
* rotation of a2 around x-axis
* rotation of a1 around y-axis
* rotation of a0 around z-axis
*
* assuming angles in range of: a0:(-pi,pi), a1:(-pi/2,pi/2), a2:(-pi/2,pi/2)
*
*/
static base::Vector3d getEuler(const base::AngleAxisd &orientation){
const Eigen::Matrix3d m = orientation.toRotationMatrix();
double x = base::Vector2d(m.coeff(2,2) , m.coeff(2,1)).norm();
base::Vector3d res(0,::atan2(-m.coeff(2,0), x),0);
if (x > Eigen::NumTraits<double>::dummy_precision()){
res[0] = ::atan2(m.coeff(1,0), m.coeff(0,0));
res[2] = ::atan2(m.coeff(2,1), m.coeff(2,2));
}else{
res[0] = 0;
res[2] = (m.coeff(2,0)>0?1:-1)* ::atan2(-m.coeff(0,1), m.coeff(1,1));
}
return res;
}示例2: return
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 /= static_cast<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);
}