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Enforce right-hand-rule on PCA eigenvectors #2946

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Mar 27, 2019
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Enforce right-hand-rule on PCA eigenvectors #2946

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ca30eb3
Enforce right-hand-rule on PCA Eigenvectors.
Danfoa Mar 26, 2019
d97bb4b
Update PCA documentation
Danfoa Mar 27, 2019
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2 changes: 2 additions & 0 deletions common/include/pcl/common/impl/pca.hpp
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Expand Up @@ -77,6 +77,8 @@ pcl::PCA<PointT>::initCompute ()
eigenvalues_[i] = evd.eigenvalues () [2-i];
eigenvectors_.col (i) = evd.eigenvectors ().col (2-i);
}
// Enforce right hand rule
eigenvectors_.col(2) = eigenvectors_.col(0).cross(eigenvectors_.col(1));
// If not basis only then compute the coefficients
if (!basis_only_)
coefficients_ = eigenvectors_.transpose() * cloud_demean.topRows<3> ();
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7 changes: 5 additions & 2 deletions common/include/pcl/common/pca.h
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Expand Up @@ -46,8 +46,10 @@ namespace pcl
/** Principal Component analysis (PCA) class.\n
* Principal components are extracted by singular values decomposition on the
* covariance matrix of the centered input cloud. Available data after pca computation
* are the mean of the input data, the eigenvalues (in descending order) and
* corresponding eigenvectors.\n
* are:\n
* - The Mean of the input data\n
* - The Eigenvectors: Ordered set of vectors representing the resultant principal components and the eigenspace cartesian basis (right-handed coordinate system).\n
* - The Eigenvalues: Eigenvectors correspondent loadings ordered in descending order.\n\n
* Other methods allow projection in the eigenspace, reconstruction from eigenspace and
* update of the eigenspace with a new datum (according Matej Artec, Matjaz Jogan and
* Ales Leonardis: "Incremental PCA for On-line Visual Learning and Recognition").
Expand Down Expand Up @@ -189,6 +191,7 @@ namespace pcl
}

/** Eigen Vectors accessor
* \return Column ordered eigenvectors, representing the eigenspace cartesian basis (right-handed coordinate system).
* \throw InitFailedException
*/
inline Eigen::Matrix3f&
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