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flare.hpp
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#ifndef PCL_FEATURES_IMPL_FLARE_H_
#define PCL_FEATURES_IMPL_FLARE_H_
#include <pcl/features/flare.h>
#include <pcl/common/geometry.h>
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> bool
pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::initCompute ()
{
if (!FeatureFromNormals<PointInT, PointNT, PointOutT>::initCompute ())
{
PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
return (false);
}
if (tangent_radius_ == 0.0f)
{
PCL_ERROR ("[pcl::%s::initCompute] tangent_radius_ not set.\n", getClassName ().c_str ());
return (false);
}
// If no search sampled_surface_ has been defined, use the surface_ dataset as the search sampled_surface_ itself
if (!sampled_surface_)
{
fake_sampled_surface_ = true;
sampled_surface_ = surface_;
if (sampled_tree_)
{
PCL_WARN ("[pcl::%s::initCompute] sampled_surface_ is not set even if sampled_tree_ is already set.", getClassName ().c_str ());
PCL_WARN ("sampled_tree_ will be rebuilt from surface_. Use sampled_surface_.\n");
}
}
// Check if a space search locator was given for sampled_surface_
if (!sampled_tree_)
{
if (sampled_surface_->isOrganized () && surface_->isOrganized () && input_->isOrganized ())
sampled_tree_.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
else
sampled_tree_.reset (new pcl::search::KdTree<PointInT> (false));
}
if (sampled_tree_->getInputCloud () != sampled_surface_) // Make sure the tree searches the sampled surface
sampled_tree_->setInputCloud (sampled_surface_);
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> bool
pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::deinitCompute ()
{
// Reset the surface
if (fake_surface_)
{
surface_.reset ();
fake_surface_ = false;
}
// Reset the sampled surface
if (fake_sampled_surface_)
{
sampled_surface_.reset ();
fake_sampled_surface_ = false;
}
return (true);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> SignedDistanceT
pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::computePointLRF (const int index,
Eigen::Matrix3f &lrf)
{
Eigen::Vector3f x_axis, y_axis;
Eigen::Vector3f fitted_normal; //z_axis
//find Z axis
//extract support points for the computation of Z axis
pcl::Indices neighbours_indices;
std::vector<float> neighbours_distances;
const std::size_t n_normal_neighbours =
this->searchForNeighbors (index, search_parameter_, neighbours_indices, neighbours_distances);
if (n_normal_neighbours < static_cast<std::size_t>(min_neighbors_for_normal_axis_))
{
if (!pcl::isFinite ((*normals_)[index]))
{
//normal is invalid
//setting lrf to NaN
lrf.setConstant (std::numeric_limits<float>::quiet_NaN ());
return (std::numeric_limits<SignedDistanceT>::max ());
}
//set z_axis as the normal of index point
fitted_normal = (*normals_)[index].getNormalVector3fMap ();
}
else
{
float plane_curvature;
normal_estimation_.computePointNormal (*surface_, neighbours_indices, fitted_normal (0), fitted_normal (1), fitted_normal (2), plane_curvature);
//disambiguate Z axis with normal mean
if (!pcl::flipNormalTowardsNormalsMean<PointNT> (*normals_, neighbours_indices, fitted_normal))
{
//all normals in the neighbourhood are invalid
//setting lrf to NaN
lrf.setConstant (std::numeric_limits<float>::quiet_NaN ());
return (std::numeric_limits<SignedDistanceT>::max ());
}
}
//setting LRF Z axis
lrf.row (2).matrix () = fitted_normal;
//find X axis
//extract support points for Rx radius
const std::size_t n_tangent_neighbours =
sampled_tree_->radiusSearch ((*input_)[index], tangent_radius_, neighbours_indices, neighbours_distances);
if (n_tangent_neighbours < static_cast<std::size_t>(min_neighbors_for_tangent_axis_))
{
//set X axis as a random axis
x_axis = pcl::geometry::randomOrthogonalAxis (fitted_normal);
y_axis = fitted_normal.cross (x_axis);
lrf.row (0).matrix () = x_axis;
lrf.row (1).matrix () = y_axis;
return (std::numeric_limits<SignedDistanceT>::max ());
}
//find point with the largest signed distance from tangent plane
SignedDistanceT shape_score;
SignedDistanceT best_shape_score = -std::numeric_limits<SignedDistanceT>::max ();
int best_shape_index = -1;
Eigen::Vector3f best_margin_point;
const float radius2 = tangent_radius_ * tangent_radius_;
const float margin_distance2 = margin_thresh_ * margin_thresh_ * radius2;
Vector3fMapConst feature_point = (*input_)[index].getVector3fMap ();
for (std::size_t curr_neigh = 0; curr_neigh < n_tangent_neighbours; ++curr_neigh)
{
const int& curr_neigh_idx = neighbours_indices[curr_neigh];
const float& neigh_distance_sqr = neighbours_distances[curr_neigh];
if (neigh_distance_sqr <= margin_distance2)
{
continue;
}
//point curr_neigh_idx is inside the ring between marginThresh and Radius
shape_score = fitted_normal.dot ((*sampled_surface_)[curr_neigh_idx].getVector3fMap ());
if (shape_score > best_shape_score)
{
best_shape_index = curr_neigh_idx;
best_shape_score = shape_score;
}
} //for each neighbor
if (best_shape_index == -1)
{
x_axis = pcl::geometry::randomOrthogonalAxis (fitted_normal);
y_axis = fitted_normal.cross (x_axis);
lrf.row (0).matrix () = x_axis;
lrf.row (1).matrix () = y_axis;
return (std::numeric_limits<SignedDistanceT>::max ());
}
//find orthogonal axis directed to best_shape_index point projection on plane with fittedNormal as axis
x_axis = pcl::geometry::projectedAsUnitVector (sampled_surface_->at (best_shape_index).getVector3fMap (), feature_point, fitted_normal);
y_axis = fitted_normal.cross (x_axis);
lrf.row (0).matrix () = x_axis;
lrf.row (1).matrix () = y_axis;
//z axis already set
best_shape_score -= fitted_normal.dot (feature_point);
return (best_shape_score);
}
//////////////////////////////////////////////////////////////////////////////////////////////
template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> void
pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::computeFeature (PointCloudOut &output)
{
//check whether used with search radius or search k-neighbors
if (this->getKSearch () != 0)
{
PCL_ERROR (
"[pcl::%s::computeFeature] Error! Search method set to k-neighborhood. Call setKSearch (0) and setRadiusSearch (radius) to use this class.\n",
getClassName ().c_str ());
return;
}
signed_distances_from_highest_points_.resize (indices_->size ());
for (std::size_t point_idx = 0; point_idx < indices_->size (); ++point_idx)
{
Eigen::Matrix3f currentLrf;
PointOutT &rf = output[point_idx];
signed_distances_from_highest_points_[point_idx] = computePointLRF ((*indices_)[point_idx], currentLrf);
if (signed_distances_from_highest_points_[point_idx] == std::numeric_limits<SignedDistanceT>::max ())
{
output.is_dense = false;
}
rf.getXAxisVector3fMap () = currentLrf.row (0);
rf.getYAxisVector3fMap () = currentLrf.row (1);
rf.getZAxisVector3fMap () = currentLrf.row (2);
}
}
#define PCL_INSTANTIATE_FLARELocalReferenceFrameEstimation(T,NT,OutT,SdT) template class PCL_EXPORTS pcl::FLARELocalReferenceFrameEstimation<T,NT,OutT,SdT>;
#endif // PCL_FEATURES_IMPL_FLARE_H_
pcl::FLARELocalReferenceFrameEstimation::deinitCompute
bool deinitCompute() override
This method should get called after the actual computation is ended.
Definition flare.hpp:91
pcl::FLARELocalReferenceFrameEstimation::computeFeature
void computeFeature(PointCloudOut &output) override
Abstract feature estimation method.
Definition flare.hpp:233
pcl::FLARELocalReferenceFrameEstimation::initCompute
bool initCompute() override
This method should get called before starting the actual computation.
Definition flare.hpp:47
pcl::FLARELocalReferenceFrameEstimation::computePointLRF
SignedDistanceT computePointLRF(const int index, Eigen::Matrix3f &lrf)
Estimate the LRF descriptor for a given point based on its spatial neighborhood of 3D points with nor...
Definition flare.hpp:110
pcl::FeatureFromNormals
Definition feature.h:309
pcl::search::KdTree
search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...
Definition kdtree.h:62
pcl::search::OrganizedNeighbor
OrganizedNeighbor is a class for optimized nearest neighbor search in organized projectable point clo...
Definition organized.h:65
geometry.h
Defines some geometrical functions and utility functions.
pcl::geometry::projectedAsUnitVector
Eigen::Vector3f projectedAsUnitVector(Eigen::Vector3f const &point, Eigen::Vector3f const &plane_origin, Eigen::Vector3f const &plane_normal)
Given a plane defined by plane_origin and plane_normal, find the unit vector pointing from plane_orig...
Definition geometry.h:115
pcl::geometry::randomOrthogonalAxis
Eigen::Vector3f randomOrthogonalAxis(Eigen::Vector3f const &axis)
Define a random unit vector orthogonal to axis.
Definition geometry.h:134
pcl::isFinite
bool isFinite(const PointT &pt)
Tests if the 3D components of a point are all finite param[in] pt point to be tested return true if f...
Definition point_tests.h:55
pcl::Vector3fMapConst
const Eigen::Map< const Eigen::Vector3f > Vector3fMapConst
Definition point_types.hpp:229
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133