| 76 | |
| 77 | template <int Dim, typename T> |
| 78 | MatrixT<T, Dim, Dim> Inverse(MatrixT<T, Dim, Dim> A) |
| 79 | { |
| 80 | /** compute SVD */ |
| 81 | Eigen::SelfAdjointEigenSolver<MatrixT<T, Dim, Dim>> SAES(A); |
| 82 | |
| 83 | /** compute tolerance (idea from OKVIS) */ |
| 84 | T Tolerance = std::numeric_limits<T>::epsilon() * A.cols() * SAES.eigenvalues().array().maxCoeff(); |
| 85 | |
| 86 | /** set small eigen values to zero */ |
| 87 | VectorT<T, Dim> EigValInv = Vector((SAES.eigenvalues().array() > Tolerance).select(SAES.eigenvalues().array().inverse(), 0)); |
| 88 | |
| 89 | /** use modified eigen values to compute sqrt */ |
| 90 | return SAES.eigenvectors() * EigValInv.asDiagonal() * SAES.eigenvectors().transpose(); |
| 91 | } |
| 92 | |
| 93 | void RobustSqrtAndInvSqrt(const Matrix &Mat, Matrix &MatSqrt, Matrix &MatSqrtInv); |
| 94 | |