Program Listing for File fpfh.h¶
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#ifndef PCL_FPFH_H_
#define PCL_FPFH_H_
#include <pcl/features/feature.h>
#include <set>
namespace pcl
{
/** \brief FPFHEstimation estimates the <b>Fast Point Feature Histogram (FPFH)</b> descriptor for a given point
* cloud dataset containing points and normals.
*
* A commonly used type for PointOutT is pcl::FPFHSignature33.
*
* \note If you use this code in any academic work, please cite:
*
* - R.B. Rusu, N. Blodow, M. Beetz.
* Fast Point Feature Histograms (FPFH) for 3D Registration.
* In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),
* Kobe, Japan, May 12-17 2009.
* - R.B. Rusu, A. Holzbach, N. Blodow, M. Beetz.
* Fast Geometric Point Labeling using Conditional Random Fields.
* In Proceedings of the 22nd IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),
* St. Louis, MO, USA, October 11-15 2009.
*
* \attention
* The convention for FPFH features is:
* - if a query point's nearest neighbors cannot be estimated, the FPFH feature will be set to NaN
* (not a number)
* - it is impossible to estimate a FPFH descriptor for a point that
* doesn't have finite 3D coordinates. Therefore, any point that contains
* NaN data on x, y, or z, will have its FPFH feature property set to NaN.
*
* \note The code is stateful as we do not expect this class to be multicore parallelized. Please look at
* \ref FPFHEstimationOMP for examples on parallel implementations of the FPFH (Fast Point Feature Histogram).
*
* \author Radu B. Rusu
* \ingroup features
*/
template <typename PointInT, typename PointNT, typename PointOutT = pcl::FPFHSignature33>
class FPFHEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
{
public:
typedef boost::shared_ptr<FPFHEstimation<PointInT, PointNT, PointOutT> > Ptr;
typedef boost::shared_ptr<const FPFHEstimation<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_parameter_;
using Feature<PointInT, PointOutT>::input_;
using Feature<PointInT, PointOutT>::surface_;
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
typedef typename Feature<PointInT, PointOutT>::PointCloudOut PointCloudOut;
/** \brief Empty constructor. */
FPFHEstimation () :
nr_bins_f1_ (11), nr_bins_f2_ (11), nr_bins_f3_ (11),
hist_f1_ (), hist_f2_ (), hist_f3_ (), fpfh_histogram_ (),
d_pi_ (1.0f / (2.0f * static_cast<float> (M_PI)))
{
feature_name_ = "FPFHEstimation";
};
/** \brief Compute the 4-tuple representation containing the three angles and one distance between two points
* represented by Cartesian coordinates and normals.
* \note For explanations about the features, please see the literature mentioned above (the order of the
* features might be different).
* \param[in] cloud the dataset containing the XYZ Cartesian coordinates of the two points
* \param[in] normals the dataset containing the surface normals (assuming normalized vectors) at each point in cloud
* \param[in] p_idx the index of the first point (source)
* \param[in] q_idx the index of the second point (target)
* \param[out] f1 the first angular feature (angle between the projection of nq_idx and u)
* \param[out] f2 the second angular feature (angle between nq_idx and v)
* \param[out] f3 the third angular feature (angle between np_idx and |p_idx - q_idx|)
* \param[out] f4 the distance feature (p_idx - q_idx)
*/
bool
computePairFeatures (const pcl::PointCloud<PointInT> &cloud, const pcl::PointCloud<PointNT> &normals,
int p_idx, int q_idx, float &f1, float &f2, float &f3, float &f4);
/** \brief Estimate the SPFH (Simple Point Feature Histograms) individual signatures of the three angular
* (f1, f2, f3) features for a given point based on its spatial neighborhood of 3D points with normals
* \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] p_idx the index of the query point (source)
* \param[in] row the index row in feature histogramms
* \param[in] indices the k-neighborhood point indices in the dataset
* \param[out] hist_f1 the resultant SPFH histogram for feature f1
* \param[out] hist_f2 the resultant SPFH histogram for feature f2
* \param[out] hist_f3 the resultant SPFH histogram for feature f3
*/
void
computePointSPFHSignature (const pcl::PointCloud<PointInT> &cloud,
const pcl::PointCloud<PointNT> &normals, int p_idx, int row,
const std::vector<int> &indices,
Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
/** \brief Weight the SPFH (Simple Point Feature Histograms) individual histograms to create the final FPFH
* (Fast Point Feature Histogram) for a given point based on its 3D spatial neighborhood
* \param[in] hist_f1 the histogram feature vector of \a f1 values over the given patch
* \param[in] hist_f2 the histogram feature vector of \a f2 values over the given patch
* \param[in] hist_f3 the histogram feature vector of \a f3 values over the given patch
* \param[in] indices the point indices of p_idx's k-neighborhood in the point cloud
* \param[in] dists the distances from p_idx to all its k-neighbors
* \param[out] fpfh_histogram the resultant FPFH histogram representing the feature at the query point
*/
void
weightPointSPFHSignature (const Eigen::MatrixXf &hist_f1,
const Eigen::MatrixXf &hist_f2,
const Eigen::MatrixXf &hist_f3,
const std::vector<int> &indices,
const std::vector<float> &dists,
Eigen::VectorXf &fpfh_histogram);
/** \brief Set the number of subdivisions for each angular feature interval.
* \param[in] nr_bins_f1 number of subdivisions for the first angular feature
* \param[in] nr_bins_f2 number of subdivisions for the second angular feature
* \param[in] nr_bins_f3 number of subdivisions for the third angular feature
*/
inline void
setNrSubdivisions (int nr_bins_f1, int nr_bins_f2, int nr_bins_f3)
{
nr_bins_f1_ = nr_bins_f1;
nr_bins_f2_ = nr_bins_f2;
nr_bins_f3_ = nr_bins_f3;
}
/** \brief Get the number of subdivisions for each angular feature interval.
* \param[out] nr_bins_f1 number of subdivisions for the first angular feature
* \param[out] nr_bins_f2 number of subdivisions for the second angular feature
* \param[out] nr_bins_f3 number of subdivisions for the third angular feature
*/
inline void
getNrSubdivisions (int &nr_bins_f1, int &nr_bins_f2, int &nr_bins_f3)
{
nr_bins_f1 = nr_bins_f1_;
nr_bins_f2 = nr_bins_f2_;
nr_bins_f3 = nr_bins_f3_;
}
protected:
/** \brief Estimate the set of all SPFH (Simple Point Feature Histograms) signatures for the input cloud
* \param[out] spf_hist_lookup a lookup table for all the SPF feature indices
* \param[out] hist_f1 the resultant SPFH histogram for feature f1
* \param[out] hist_f2 the resultant SPFH histogram for feature f2
* \param[out] hist_f3 the resultant SPFH histogram for feature f3
*/
void
computeSPFHSignatures (std::vector<int> &spf_hist_lookup,
Eigen::MatrixXf &hist_f1, Eigen::MatrixXf &hist_f2, Eigen::MatrixXf &hist_f3);
/** \brief Estimate the Fast Point Feature Histograms (FPFH) 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 FPFH feature estimates
*/
void
computeFeature (PointCloudOut &output);
/** \brief The number of subdivisions for each angular feature interval. */
int nr_bins_f1_, nr_bins_f2_, nr_bins_f3_;
/** \brief Placeholder for the f1 histogram. */
Eigen::MatrixXf hist_f1_;
/** \brief Placeholder for the f2 histogram. */
Eigen::MatrixXf hist_f2_;
/** \brief Placeholder for the f3 histogram. */
Eigen::MatrixXf hist_f3_;
/** \brief Placeholder for a point's FPFH signature. */
Eigen::VectorXf fpfh_histogram_;
/** \brief Float constant = 1.0 / (2.0 * M_PI) */
float d_pi_;
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/fpfh.hpp>
#endif
#endif //#ifndef PCL_FPFH_H_