\brief Filter cloud and initializes voxel structure. * \param[out] output cloud containing centroids of voxels containing a sufficient number o

align point clouds and measure processing time

\brief Provide a pointer to the input dataset * \param cloud the const boost shared pointer to a PointCloud message */

\brief Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to). * \param[in] cloud the input point

\brief Get the inverse of the voxel covariance. * \return inverse covariance matrix */

\brief Get the voxel centroid. * \return centroid */

\brief Search for all the nearest occupied voxels of the query point in a given radius. * \note Only voxels containing a sufficient number of p

\brief Set/change the voxel grid resolution. * \param[in] resolution side length of voxels */

\brief Search for the k-nearest occupied voxels for the given query point. * \note Only voxels containing a sufficient number of points are use

\brief Empty constructor. */

\brief Constructor. * Sets \ref nr_points, \ref icov_, \ref mean_ and \ref evals_ to 0 and \ref cov_ and \ref evecs_ to the identity matrix

\brief Constructor. * Sets \ref leaf_size_ to 0 and \ref searchable_ to false. */

\brief Auxilary function used to determin endpoints of More-Thuente interval. * \note \f$ \psi(\alpha) \f$ in Equation 1.6 (Moore, Thuente 1994)

\brief Auxilary function derivative used to determin endpoints of More-Thuente interval. * \note \f$ \psi'(\alpha) \f$, derivative of Equation 1.6

\brief Estimate the transformation and returns the transformed source (input) as output. * \param[out] output the resultant input transfomed point

\brief Convert 6 element transformation vector to affine transformation. * \param[in] x transformation vector of the form [x, y, z, roll, pitch, y

read data and add data

\brief Get a pointcloud containing the voxel centroids * \note Only voxels containing a sufficient number of points are used. * \return

\brief Get the number of neighbors used when computing covariances as set by * the user */

\brief Get the voxel covariance. * \return covariance matrix */

\brief Get the minimum allowable ratio between eigenvalues to prevent singular covariance matrices. * \return the minimum allowable ratio betw

\brief Get the eigen values of the voxel covariance. * \note Order corresponds with \ref getEvecs * \return vector of eigen values

\brief Get the eigen vectors of the voxel covariance. * \note Order corresponds with \ref getEvals * \return matrix whose columns

\brief Get the number of iterations required to calculate alignment. * \return final number of iterations */

\brief Get the voxel containing point p. * \param[in] index the index of the leaf structure node * \return const pointer to leaf structu

\return maximum number of iterations at the optimization step

\brief Get the minimum number of points required for a cell to be used. * \return the minimum number of points for required for a voxel to be

\brief Get the point cloud outlier ratio. * \return outlier ratio */

\brief Get the number of points contained by this voxel. * \return number of points */

\brief Get voxel grid resolution. * \return side length of voxels */

\brief Get the rotation epsilon (maximum allowable difference between two * consecutive rotations) as set by the user. */

\brief Get the newton line search maximum step length. * \return maximum step length */

\brief Get the registration alignment probability. * \return transformation probability */

\brief Initiate covariance voxel structure. */

\return trace of mat1^t . mat2 * \param mat1 matrix of dimension nxm * \param mat2 matrix of dimension nxp */

\brief Search for the closest nearest neighbor of a given point. * \param query the point to search a nearest neighbour for * \param i

\brief Set the number of neighbors used when selecting a point neighbourhood * to compute covariances. * A higher value will bring mor

\brief Set the minimum allowable ratio between eigenvalues to prevent singular covariance matrices. * \param[in] min_covar_eigvalue_mult the m

\brief Provide a pointer to the input target (e.g., the point cloud that we want to align the input source to) * \param[in] target the input p

set maximum number of iterations at the optimization step * \param[in] max maximum number of iterations for the optimizer */

\brief Set the minimum number of points required for a cell to be used (must be 3 or greater for covariance calculation). * \param[in] min_poi

\brief Set/change the point cloud outlier ratio. * \param[in] outlier_ratio outlier ratio */

\brief Set the rotation epsilon (maximum allowable difference between two * consecutive rotations) in order for an optimization to be consider

\brief Provide a pointer to the covariances of the input source (if computed externally!). * If not set, GeneralizedIterativeClosestPoint will

\brief Set/change the newton line search maximum step length. * \param[in] step_size maximum step length */

\brief Provide a pointer to the covariances of the input target (if computed externally!). * If not set, GeneralizedIterativeClosestPoint will

\brief Empty destructor */