Program Listing for File vfh.h¶
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#ifndef PCL_FEATURES_VFH_H_
#define PCL_FEATURES_VFH_H_
#include <pcl/point_types.h>
#include <pcl/features/feature.h>
namespace pcl
{
/** \brief VFHEstimation estimates the <b>Viewpoint Feature Histogram (VFH)</b> descriptor for a given point cloud
* dataset containing points and normals. The default VFH implementation uses 45 binning subdivisions for each of
* the three extended FPFH values, plus another 45 binning subdivisions for the distances between each point and
* the centroid and 128 binning subdivisions for the viewpoint component, which results in a
* 308-byte array of float values. These are stored in a pcl::VFHSignature308 point type.
* A major difference between the PFH/FPFH descriptors and VFH, is that for a given point cloud dataset, only a
* single VFH descriptor will be estimated (vfhs->points.size() should be 1), while the resultant PFH/FPFH data
* will have the same number of entries as the number of points in the cloud.
*
* \note If you use this code in any academic work, please cite:
*
* - R.B. Rusu, G. Bradski, R. Thibaux, J. Hsu.
* Fast 3D Recognition and Pose Using the Viewpoint Feature Histogram.
* In Proceedings of International Conference on Intelligent Robots and Systems (IROS)
* Taipei, Taiwan, October 18-22 2010.
*
* \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
* \ref FPFHEstimationOMP for an example of a parallel implementation of the FPFH (Fast Point Feature Histogram).
* \author Radu B. Rusu
* \ingroup features
*/
template<typename PointInT, typename PointNT, typename PointOutT = pcl::VFHSignature308>
class VFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
{
public:
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>::input_;
using Feature<PointInT, PointOutT>::surface_;
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
typedef typename boost::shared_ptr<VFHEstimation<PointInT, PointNT, PointOutT> > Ptr;
typedef typename boost::shared_ptr<const VFHEstimation<PointInT, PointNT, PointOutT> > ConstPtr;
/** \brief Empty constructor. */
VFHEstimation () :
nr_bins_f1_ (45), nr_bins_f2_ (45), nr_bins_f3_ (45), nr_bins_f4_ (45), nr_bins_vp_ (128),
vpx_ (0), vpy_ (0), vpz_ (0),
hist_f1_ (), hist_f2_ (), hist_f3_ (), hist_f4_ (), hist_vp_ (),
normal_to_use_ (), centroid_to_use_ (), use_given_normal_ (false), use_given_centroid_ (false),
normalize_bins_ (true), normalize_distances_ (false), size_component_ (false),
d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI)))
{
hist_f1_.setZero (nr_bins_f1_);
hist_f2_.setZero (nr_bins_f2_);
hist_f3_.setZero (nr_bins_f3_);
hist_f4_.setZero (nr_bins_f4_);
search_radius_ = 0;
k_ = 0;
feature_name_ = "VFHEstimation";
}
/** \brief Estimate the SPFH (Simple Point Feature Histograms) signatures of the angular
* (f1, f2, f3) and distance (f4) features for a given point from its neighborhood
* \param[in] centroid_p the centroid point
* \param[in] centroid_n the centroid normal
* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
* \param[in] normals the dataset containing the surface normals at each point in \a cloud
* \param[in] indices the k-neighborhood point indices in the dataset
*/
void
computePointSPFHSignature (const Eigen::Vector4f ¢roid_p, const Eigen::Vector4f ¢roid_n,
const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
const std::vector<int> &indices);
/** \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 Get the viewpoint. */
inline void
getViewPoint (float &vpx, float &vpy, float &vpz)
{
vpx = vpx_;
vpy = vpy_;
vpz = vpz_;
}
/** \brief Set use_given_normal_
* \param[in] use Set to true if you want to use the normal passed to setNormalUse(normal)
*/
inline void
setUseGivenNormal (bool use)
{
use_given_normal_ = use;
}
/** \brief Set the normal to use
* \param[in] normal Sets the normal to be used in the VFH computation. It is is used
* to build the Darboux Coordinate system.
*/
inline void
setNormalToUse (const Eigen::Vector3f &normal)
{
normal_to_use_ = Eigen::Vector4f (normal[0], normal[1], normal[2], 0);
}
/** \brief Set use_given_centroid_
* \param[in] use Set to true if you want to use the centroid passed through setCentroidToUse(centroid)
*/
inline void
setUseGivenCentroid (bool use)
{
use_given_centroid_ = use;
}
/** \brief Set centroid_to_use_
* \param[in] centroid Centroid to be used in the VFH computation. It is used to compute the distances
* from all points to this centroid.
*/
inline void
setCentroidToUse (const Eigen::Vector3f ¢roid)
{
centroid_to_use_ = Eigen::Vector4f (centroid[0], centroid[1], centroid[2], 0);
}
/** \brief set normalize_bins_
* \param[in] normalize If true, the VFH bins are normalized using the total number of points
*/
inline void
setNormalizeBins (bool normalize)
{
normalize_bins_ = normalize;
}
/** \brief set normalize_distances_
* \param[in] normalize If true, the 4th component of VFH (shape distribution component) get normalized
* by the maximum size between the centroid and the point cloud
*/
inline void
setNormalizeDistance (bool normalize)
{
normalize_distances_ = normalize;
}
/** \brief set size_component_
* \param[in] fill_size True if the 4th component of VFH (shape distribution component) needs to be filled.
* Otherwise, it is set to zero.
*/
inline void
setFillSizeComponent (bool fill_size)
{
size_component_ = fill_size;
}
/** \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 The number of subdivisions for each feature interval. */
int nr_bins_f1_, nr_bins_f2_, nr_bins_f3_, nr_bins_f4_, nr_bins_vp_;
/** \brief Values describing the viewpoint ("pinhole" camera model assumed). For per point viewpoints, inherit
* from VFHEstimation and provide your own computeFeature (). By default, the viewpoint is set to 0,0,0.
*/
float vpx_, vpy_, vpz_;
/** \brief Estimate the Viewpoint Feature Histograms (VFH) descriptors at a set of points given by
* <setInputCloud (), setIndices ()> using the surface in setSearchSurface () and the spatial locator in
* setSearchMethod ()
* \param[out] output the resultant point cloud model dataset that contains the VFH feature estimates
*/
void
computeFeature (PointCloudOut &output);
protected:
/** \brief This method should get called before starting the actual computation. */
bool
initCompute ();
/** \brief Placeholder for the f1 histogram. */
Eigen::VectorXf hist_f1_;
/** \brief Placeholder for the f2 histogram. */
Eigen::VectorXf hist_f2_;
/** \brief Placeholder for the f3 histogram. */
Eigen::VectorXf hist_f3_;
/** \brief Placeholder for the f4 histogram. */
Eigen::VectorXf hist_f4_;
/** \brief Placeholder for the vp histogram. */
Eigen::VectorXf hist_vp_;
/** \brief Normal to be used to computed VFH. Default, the average normal of the whole point cloud */
Eigen::Vector4f normal_to_use_;
/** \brief Centroid to be used to computed VFH. Default, the centroid of the whole point cloud */
Eigen::Vector4f centroid_to_use_;
// VFH configuration parameters because CVFH instantiates it. See constructor for default values.
/** \brief Use the normal_to_use_ */
bool use_given_normal_;
/** \brief Use the centroid_to_use_ */
bool use_given_centroid_;
/** \brief Normalize bins by the number the total number of points. */
bool normalize_bins_;
/** \brief Normalize the shape distribution component of VFH */
bool normalize_distances_;
/** \brief Activate or deactivate the size component of VFH */
bool size_component_;
private:
/** \brief Float constant = 1.0 / (2.0 * M_PI) */
float d_pi_;
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/vfh.hpp>
#endif
#endif //#ifndef PCL_FEATURES_VFH_H_