Example #1
0
 /*
  * 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;
 }
Example #2
0
template <typename PointT> inline unsigned int
pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
                                const std::vector<int> &indices,
                                Eigen::Matrix3d &covariance_matrix,
                                Eigen::Vector4d &centroid)
{
  // 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);
}
Example #3
0
template <typename PointT> inline unsigned int
pcl::computeMeanAndCovarianceMatrix (const pcl::PointCloud<PointT> &cloud,
                                     Eigen::Matrix3d &covariance_matrix,
                                     Eigen::Vector4d &centroid)
{
  // 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 /= static_cast<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);
}