matrix_3f.h

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/** \author Jia Pan */

#ifndef FCL_MATRIX_3F_H
#define FCL_MATRIX_3F_H

#include <hpp/fcl/math/vec_3f.h>

#if FCL_USE_NATIVE_EIGEN
# include <hpp/fcl/eigen/matrix_3fx.h>
#else
# include <hpp/fcl/math/matrix_3fx.h>
#endif

namespace fcl
{

#if FCL_HAVE_EIGEN
# if FCL_USE_NATIVE_EIGEN
  typedef Eigen::FclMatrix<FCL_REAL, 3, 0> Matrix3f;
# else
  typedef Matrix3fX<details::eigen_wrapper_m3<FCL_REAL> > Matrix3f;
# endif
#elif FCL_HAVE_SSE
typedef Matrix3fX<details::sse_meta_f12> Matrix3f;
#else
typedef Matrix3fX<details::Matrix3Data<FCL_REAL> > Matrix3f;
#endif

static inline std::ostream& operator << (std::ostream& o, const Matrix3f& m)
{
  o << "[(" << m(0, 0) << " " << m(0, 1) << " " << m(0, 2) << ")("
    << m(1, 0) << " " << m(1, 1) << " " << m(1, 2) << ")(" 
    << m(2, 0) << " " << m(2, 1) << " " << m(2, 2) << ")]";
  return o;
}



/// @brief Class for variance matrix in 3d
class Variance3f
{
public:
  /// @brief Variation matrix
  Matrix3f Sigma;

  /// @brief Variations along the eign axes
  Matrix3f::U sigma[3];

  /// @brief Eigen axes of the variation matrix
  Vec3f axis[3];

  Variance3f() {}

  Variance3f(const Matrix3f& S) : Sigma(S)
  {
    init();
  }

  /// @brief init the Variance
  void init() 
  {
    eigen(Sigma, sigma, axis);
  }

  /// @brief Compute the sqrt of Sigma matrix based on the eigen decomposition result, this is useful when the uncertainty matrix is initialized as a square variation matrix
  Variance3f& sqrt()
  {
    for(std::size_t i = 0; i < 3; ++i)
    {
      if(sigma[i] < 0) sigma[i] = 0;
      sigma[i] = std::sqrt(sigma[i]);
    }


    Sigma.setZero();
    for(std::size_t i = 0; i < 3; ++i)
    {
      for(std::size_t j = 0; j < 3; ++j)
      {
        Sigma(i, j) += sigma[0] * axis[0][i] * axis[0][j];
        Sigma(i, j) += sigma[1] * axis[1][i] * axis[1][j];
        Sigma(i, j) += sigma[2] * axis[2][i] * axis[2][j];
      }
    }

    return *this;
  }
};

}



#endif