Program Listing for File cvfh.h¶
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#ifndef PCL_FEATURES_CVFH_H_
#define PCL_FEATURES_CVFH_H_
#include <pcl/features/feature.h>
#include <pcl/features/vfh.h>
#include <pcl/search/pcl_search.h>
#include <pcl/common/common.h>
namespace pcl
{
/** \brief CVFHEstimation estimates the Clustered Viewpoint Feature Histogram (CVFH) descriptor for a given
* point cloud dataset containing XYZ data and normals, as presented in:
* - CAD-Model Recognition and 6 DOF Pose Estimation
* A. Aldoma, N. Blodow, D. Gossow, S. Gedikli, R.B. Rusu, M. Vincze and G. Bradski
* ICCV 2011, 3D Representation and Recognition (3dRR11) workshop
* Barcelona, Spain, (2011)
*
* The suggested PointOutT is pcl::VFHSignature308.
*
* \author Aitor Aldoma
* \ingroup features
*/
template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308>
class CVFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
{
public:
typedef boost::shared_ptr<CVFHEstimation<PointInT, PointNT, PointOutT> > Ptr;
typedef boost::shared_ptr<const CVFHEstimation<PointInT, PointNT, PointOutT> > ConstPtr;
using Feature<PointInT, PointOutT>::feature_name_;
using Feature<PointInT, PointOutT>::getClassName;
using Feature<PointInT, PointOutT>::indices_;
using Feature<PointInT, PointOutT>::k_;
using Feature<PointInT, PointOutT>::search_radius_;
using Feature<PointInT, PointOutT>::surface_;
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
typedef typename pcl::search::Search<PointNormal>::Ptr KdTreePtr;
typedef typename pcl::VFHEstimation<PointInT, PointNT, pcl::VFHSignature308> VFHEstimator;
/** \brief Empty constructor. */
CVFHEstimation () :
vpx_ (0), vpy_ (0), vpz_ (0),
leaf_size_ (0.005f),
normalize_bins_ (false),
curv_threshold_ (0.03f),
cluster_tolerance_ (leaf_size_ * 3),
eps_angle_threshold_ (0.125f),
min_points_ (50),
radius_normals_ (leaf_size_ * 3),
centroids_dominant_orientations_ (),
dominant_normals_ ()
{
search_radius_ = 0;
k_ = 1;
feature_name_ = "CVFHEstimation";
}
;
/** \brief Removes normals with high curvature caused by real edges or noisy data
* \param[in] cloud pointcloud to be filtered
* \param[in] indices_to_use the indices to use
* \param[out] indices_out the indices of the points with higher curvature than threshold
* \param[out] indices_in the indices of the remaining points after filtering
* \param[in] threshold threshold value for curvature
*/
void
filterNormalsWithHighCurvature (const pcl::PointCloud<PointNT> & cloud, std::vector<int> & indices_to_use, std::vector<int> &indices_out,
std::vector<int> &indices_in, float threshold);
/** \brief Set the viewpoint.
* \param[in] vpx the X coordinate of the viewpoint
* \param[in] vpy the Y coordinate of the viewpoint
* \param[in] vpz the Z coordinate of the viewpoint
*/
inline void
setViewPoint (float vpx, float vpy, float vpz)
{
vpx_ = vpx;
vpy_ = vpy;
vpz_ = vpz;
}
/** \brief Set the radius used to compute normals
* \param[in] radius_normals the radius
*/
inline void
setRadiusNormals (float radius_normals)
{
radius_normals_ = radius_normals;
}
/** \brief Get the viewpoint.
* \param[out] vpx the X coordinate of the viewpoint
* \param[out] vpy the Y coordinate of the viewpoint
* \param[out] vpz the Z coordinate of the viewpoint
*/
inline void
getViewPoint (float &vpx, float &vpy, float &vpz)
{
vpx = vpx_;
vpy = vpy_;
vpz = vpz_;
}
/** \brief Get the centroids used to compute different CVFH descriptors
* \param[out] centroids vector to hold the centroids
*/
inline void
getCentroidClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
{
for (size_t i = 0; i < centroids_dominant_orientations_.size (); ++i)
centroids.push_back (centroids_dominant_orientations_[i]);
}
/** \brief Get the normal centroids used to compute different CVFH descriptors
* \param[out] centroids vector to hold the normal centroids
*/
inline void
getCentroidNormalClusters (std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > & centroids)
{
for (size_t i = 0; i < dominant_normals_.size (); ++i)
centroids.push_back (dominant_normals_[i]);
}
/** \brief Sets max. Euclidean distance between points to be added to the cluster
* \param[in] d the maximum Euclidean distance
*/
inline void
setClusterTolerance (float d)
{
cluster_tolerance_ = d;
}
/** \brief Sets max. deviation of the normals between two points so they can be clustered together
* \param[in] d the maximum deviation
*/
inline void
setEPSAngleThreshold (float d)
{
eps_angle_threshold_ = d;
}
/** \brief Sets curvature threshold for removing normals
* \param[in] d the curvature threshold
*/
inline void
setCurvatureThreshold (float d)
{
curv_threshold_ = d;
}
/** \brief Set minimum amount of points for a cluster to be considered
* \param[in] min the minimum amount of points to be set
*/
inline void
setMinPoints (size_t min)
{
min_points_ = min;
}
/** \brief Sets whether if the CVFH signatures should be normalized or not
* \param[in] normalize true if normalization is required, false otherwise
*/
inline void
setNormalizeBins (bool normalize)
{
normalize_bins_ = normalize;
}
/** \brief Overloaded computed method from pcl::Feature.
* \param[out] output the resultant point cloud model dataset containing the estimated features
*/
void
compute (PointCloudOut &output);
private:
/** \brief Values describing the viewpoint ("pinhole" camera model assumed).
* By default, the viewpoint is set to 0,0,0.
*/
float vpx_, vpy_, vpz_;
/** \brief Size of the voxels after voxel gridding. IMPORTANT: Must match the voxel
* size of the training data or the normalize_bins_ flag must be set to true.
*/
float leaf_size_;
/** \brief Whether to normalize the signatures or not. Default: false. */
bool normalize_bins_;
/** \brief Curvature threshold for removing normals. */
float curv_threshold_;
/** \brief allowed Euclidean distance between points to be added to the cluster. */
float cluster_tolerance_;
/** \brief deviation of the normals between two points so they can be clustered together. */
float eps_angle_threshold_;
/** \brief Minimum amount of points in a clustered region to be considered stable for CVFH
* computation.
*/
size_t min_points_;
/** \brief Radius for the normals computation. */
float radius_normals_;
/** \brief Estimate the Clustered Viewpoint Feature Histograms (CVFH) descriptors at
* a set of points given by <setInputCloud (), setIndices ()> using the surface in
* setSearchSurface ()
*
* \param[out] output the resultant point cloud model dataset that contains the CVFH
* feature estimates
*/
void
computeFeature (PointCloudOut &output);
/** \brief Region growing method using Euclidean distances and neighbors normals to
* add points to a region.
* \param[in] cloud point cloud to split into regions
* \param[in] normals are the normals of cloud
* \param[in] tolerance is the allowed Euclidean distance between points to be added to
* the cluster
* \param[in] tree is the spatial search structure for nearest neighbour search
* \param[out] clusters vector of indices representing the clustered regions
* \param[in] eps_angle deviation of the normals between two points so they can be
* clustered together
* \param[in] min_pts_per_cluster minimum cluster size. (default: 1 point)
* \param[in] max_pts_per_cluster maximum cluster size. (default: all the points)
*/
void
extractEuclideanClustersSmooth (const pcl::PointCloud<pcl::PointNormal> &cloud,
const pcl::PointCloud<pcl::PointNormal> &normals, float tolerance,
const pcl::search::Search<pcl::PointNormal>::Ptr &tree,
std::vector<pcl::PointIndices> &clusters, double eps_angle,
unsigned int min_pts_per_cluster = 1,
unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ());
protected:
/** \brief Centroids that were used to compute different CVFH descriptors */
std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > centroids_dominant_orientations_;
/** \brief Normal centroids that were used to compute different CVFH descriptors */
std::vector<Eigen::Vector3f, Eigen::aligned_allocator<Eigen::Vector3f> > dominant_normals_;
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/cvfh.hpp>
#endif
#endif //#ifndef PCL_FEATURES_CVFH_H_