Program Listing for File flare.h¶
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#ifndef PCL_FLARE_H_
#define PCL_FLARE_H_
#include <pcl/point_types.h>
#include <pcl/features/feature.h>
#include <pcl/features/normal_3d.h>
namespace pcl
{
/** \brief FLARELocalReferenceFrameEstimation implements the Fast LocAl Reference framE algorithm
* for local reference frame estimation as described here:
*
* - A. Petrelli, L. Di Stefano,
* "A repeatable and efficient canonical reference for surface matching",
* 3DimPVT, 2012
*
* FLARE algorithm is deployed in ReLOC algorithm proposed in:
*
* Petrelli A., Di Stefano L., "Pairwise registration by local orientation cues", Computer Graphics Forum, 2015.
*
* \author Alioscia Petrelli
* \ingroup features
*/
template<typename PointInT, typename PointNT, typename PointOutT = ReferenceFrame, typename SignedDistanceT = float>
class FLARELocalReferenceFrameEstimation : public FeatureFromNormals<PointInT, PointNT, PointOutT>
{
protected:
using Feature<PointInT, PointOutT>::feature_name_;
using Feature<PointInT, PointOutT>::input_;
using Feature<PointInT, PointOutT>::indices_;
using Feature<PointInT, PointOutT>::surface_;
using Feature<PointInT, PointOutT>::tree_;
using Feature<PointInT, PointOutT>::search_parameter_;
using FeatureFromNormals<PointInT, PointNT, PointOutT>::normals_;
using Feature<PointInT, PointOutT>::fake_surface_;
using Feature<PointInT, PointOutT>::getClassName;
using typename Feature<PointInT, PointOutT>::PointCloudIn;
using typename Feature<PointInT, PointOutT>::PointCloudOut;
using typename Feature<PointInT, PointOutT>::PointCloudInConstPtr;
using typename Feature<PointInT, PointOutT>::KdTreePtr;
typedef typename pcl::PointCloud<SignedDistanceT> PointCloudSignedDistance;
typedef typename PointCloudSignedDistance::Ptr PointCloudSignedDistancePtr;
typedef boost::shared_ptr<FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT> > Ptr;
typedef boost::shared_ptr<const FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT> > ConstPtr;
public:
/** \brief Constructor. */
FLARELocalReferenceFrameEstimation () :
tangent_radius_ (0.0f),
margin_thresh_ (0.85f),
min_neighbors_for_normal_axis_ (6),
min_neighbors_for_tangent_axis_ (6),
sampled_surface_ (),
sampled_tree_ (),
fake_sampled_surface_ (false)
{
feature_name_ = "FLARELocalReferenceFrameEstimation";
}
//Getters/Setters
/** \brief Set the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a given point.
*
* \param[in] radius The search radius for x axis.
*/
inline void
setTangentRadius (float radius)
{
tangent_radius_ = radius;
}
/** \brief Get the maximum distance of the points used to estimate the x_axis of the FLARE Reference Frame for a given point.
*
* \return The search radius for x axis.
*/
inline float
getTangentRadius () const
{
return (tangent_radius_);
}
/** \brief Set the percentage of the search tangent radius after which a point is considered part of the support.
*
* \param[in] margin_thresh the percentage of the search tangent radius after which a point is considered part of the support.
*/
inline void
setMarginThresh (float margin_thresh)
{
margin_thresh_ = margin_thresh;
}
/** \brief Get the percentage of the search tangent radius after which a point is considered part of the support.
*
* \return The percentage of the search tangent radius after which a point is considered part of the support.
*/
inline float
getMarginThresh () const
{
return (margin_thresh_);
}
/** \brief Set min number of neighbours required for the computation of Z axis.
*
* \param[in] min_neighbors_for_normal_axis min number of neighbours required for the computation of Z axis.
*/
inline void
setMinNeighboursForNormalAxis (int min_neighbors_for_normal_axis)
{
min_neighbors_for_normal_axis_ = min_neighbors_for_normal_axis;
}
/** \brief Get min number of neighbours required for the computation of Z axis.
*
* \return min number of neighbours required for the computation of Z axis.
*/
inline int
getMinNeighboursForNormalAxis () const
{
return (min_neighbors_for_normal_axis_);
}
/** \brief Set min number of neighbours required for the computation of X axis.
*
* \param[in] min_neighbors_for_tangent_axis min number of neighbours required for the computation of X axis.
*/
inline void
setMinNeighboursForTangentAxis (int min_neighbors_for_tangent_axis)
{
min_neighbors_for_tangent_axis_ = min_neighbors_for_tangent_axis;
}
/** \brief Get min number of neighbours required for the computation of X axis.
*
* \return min number of neighbours required for the computation of X axis.
*/
inline int
getMinNeighboursForTangentAxis () const
{
return (min_neighbors_for_tangent_axis_);
}
/** \brief Provide a pointer to the dataset used for the estimation of X axis.
* As the estimation of x axis is negligibly affected by surface downsampling,
* this method lets to consider a downsampled version of surface_ in the estimation of x axis.
* This is optional, if this is not set, it will only use the data in the
* surface_ cloud to estimate the x axis.
* \param[in] cloud a pointer to a PointCloud
*/
inline void
setSearchSampledSurface(const PointCloudInConstPtr &cloud)
{
sampled_surface_ = cloud;
fake_sampled_surface_ = false;
}
/** \brief Get a pointer to the sampled_surface_ cloud dataset. */
inline const PointCloudInConstPtr&
getSearchSampledSurface() const
{
return (sampled_surface_);
}
/** \brief Provide a pointer to the search object linked to sampled_surface.
* \param[in] tree a pointer to the spatial search object linked to sampled_surface.
*/
inline void
setSearchMethodForSampledSurface (const KdTreePtr &tree) { sampled_tree_ = tree; }
/** \brief Get a pointer to the search method used for the extimation of x axis. */
inline const KdTreePtr&
getSearchMethodForSampledSurface () const
{
return (sampled_tree_);
}
/** \brief Get the signed distances of the highest points from the fitted planes. */
inline const std::vector<SignedDistanceT> &
getSignedDistancesFromHighestPoints () const
{
return (signed_distances_from_highest_points_);
}
protected:
/** \brief This method should get called before starting the actual computation. */
virtual bool
initCompute ();
/** \brief This method should get called after the actual computation is ended. */
virtual bool
deinitCompute ();
/** \brief Estimate the LRF descriptor for a given point based on its spatial neighborhood of 3D points with normals
* \param[in] index the index of the point in input_
* \param[out] lrf the resultant local reference frame
* \return signed distance of the highest point from the fitted plane. Max if the lrf is not computable.
*/
SignedDistanceT
computePointLRF (const int index, Eigen::Matrix3f &lrf);
/** \brief Abstract feature estimation method.
* \param[out] output the resultant features
*/
virtual void
computeFeature (PointCloudOut &output);
private:
/** \brief Radius used to find tangent axis. */
float tangent_radius_;
/** \brief Threshold that define if a support point is near the margins. */
float margin_thresh_;
/** \brief Min number of neighbours required for the computation of Z axis. Otherwise, feature point normal is used. */
int min_neighbors_for_normal_axis_;
/** \brief Min number of neighbours required for the computation of X axis. Otherwise, a random X axis is set */
int min_neighbors_for_tangent_axis_;
/** \brief An input point cloud describing the surface that is to be used
* for nearest neighbor searches for the estimation of X axis.
*/
PointCloudInConstPtr sampled_surface_;
/** \brief A pointer to the spatial search object used for the estimation of X axis. */
KdTreePtr sampled_tree_;
/** \brief Class for normal estimation. */
NormalEstimation<PointInT, PointNT> normal_estimation_;
/** \brief Signed distances of the highest points from the fitted planes.*/
std::vector<SignedDistanceT> signed_distances_from_highest_points_;
/** \brief If no sampled_surface_ is given, we use surface_ as the sampled surface. */
bool fake_sampled_surface_;
};
}
#ifdef PCL_NO_PRECOMPILE
#include <pcl/features/impl/flare.hpp>
#endif
#endif //#ifndef PCL_FLARE_H_