266 lines
9.5 KiB
C++
266 lines
9.5 KiB
C++
/*
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* Software License Agreement (BSD License)
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*
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* Point Cloud Library (PCL) - www.pointclouds.org
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* Copyright (c) 2016-, Open Perception, Inc.
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*
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* All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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* are met:
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*
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* * Redistributions of source code must retain the above copyright
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* notice, this list of conditions and the following disclaimer.
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* * Redistributions in binary form must reproduce the above
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* copyright notice, this list of conditions and the following
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* disclaimer in the documentation and/or other materials provided
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* with the distribution.
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* * Neither the name of the copyright holder(s) nor the names of its
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* contributors may be used to endorse or promote products derived
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* from this software without specific prior written permission.
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*
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* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
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* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
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* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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* POSSIBILITY OF SUCH DAMAGE.
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*
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*
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*/
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#ifndef PCL_FEATURES_IMPL_FLARE_H_
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#define PCL_FEATURES_IMPL_FLARE_H_
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#include <pcl/features/flare.h>
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#include <pcl/common/geometry.h>
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> bool
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pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::initCompute ()
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{
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if (!FeatureFromNormals<PointInT, PointNT, PointOutT>::initCompute ())
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{
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PCL_ERROR ("[pcl::%s::initCompute] Init failed.\n", getClassName ().c_str ());
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return (false);
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}
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if (tangent_radius_ == 0.0f)
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{
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PCL_ERROR ("[pcl::%s::initCompute] tangent_radius_ not set.\n", getClassName ().c_str ());
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return (false);
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}
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// If no search sampled_surface_ has been defined, use the surface_ dataset as the search sampled_surface_ itself
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if (!sampled_surface_)
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{
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fake_sampled_surface_ = true;
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sampled_surface_ = surface_;
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if (sampled_tree_)
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{
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PCL_WARN ("[pcl::%s::initCompute] sampled_surface_ is not set even if sampled_tree_ is already set.", getClassName ().c_str ());
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PCL_WARN ("sampled_tree_ will be rebuilt from surface_. Use sampled_surface_.\n");
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}
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}
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// Check if a space search locator was given for sampled_surface_
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if (!sampled_tree_)
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{
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if (sampled_surface_->isOrganized () && surface_->isOrganized () && input_->isOrganized ())
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sampled_tree_.reset (new pcl::search::OrganizedNeighbor<PointInT> ());
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else
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sampled_tree_.reset (new pcl::search::KdTree<PointInT> (false));
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}
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if (sampled_tree_->getInputCloud () != sampled_surface_) // Make sure the tree searches the sampled surface
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sampled_tree_->setInputCloud (sampled_surface_);
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> bool
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pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::deinitCompute ()
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{
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// Reset the surface
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if (fake_surface_)
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{
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surface_.reset ();
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fake_surface_ = false;
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}
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// Reset the sampled surface
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if (fake_sampled_surface_)
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{
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sampled_surface_.reset ();
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fake_sampled_surface_ = false;
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}
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return (true);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> SignedDistanceT
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pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::computePointLRF (const int index,
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Eigen::Matrix3f &lrf)
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{
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Eigen::Vector3f x_axis, y_axis;
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Eigen::Vector3f fitted_normal; //z_axis
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//find Z axis
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//extract support points for the computation of Z axis
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pcl::Indices neighbours_indices;
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std::vector<float> neighbours_distances;
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const std::size_t n_normal_neighbours =
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this->searchForNeighbors (index, search_parameter_, neighbours_indices, neighbours_distances);
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if (n_normal_neighbours < static_cast<std::size_t>(min_neighbors_for_normal_axis_))
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{
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if (!pcl::isFinite ((*normals_)[index]))
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{
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//normal is invalid
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//setting lrf to NaN
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lrf.setConstant (std::numeric_limits<float>::quiet_NaN ());
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return (std::numeric_limits<SignedDistanceT>::max ());
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}
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//set z_axis as the normal of index point
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fitted_normal = (*normals_)[index].getNormalVector3fMap ();
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}
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else
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{
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float plane_curvature;
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normal_estimation_.computePointNormal (*surface_, neighbours_indices, fitted_normal (0), fitted_normal (1), fitted_normal (2), plane_curvature);
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//disambiguate Z axis with normal mean
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if (!pcl::flipNormalTowardsNormalsMean<PointNT> (*normals_, neighbours_indices, fitted_normal))
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{
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//all normals in the neighbourood are invalid
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//setting lrf to NaN
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lrf.setConstant (std::numeric_limits<float>::quiet_NaN ());
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return (std::numeric_limits<SignedDistanceT>::max ());
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}
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}
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//setting LRF Z axis
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lrf.row (2).matrix () = fitted_normal;
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//find X axis
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//extract support points for Rx radius
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const std::size_t n_tangent_neighbours =
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sampled_tree_->radiusSearch ((*input_)[index], tangent_radius_, neighbours_indices, neighbours_distances);
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if (n_tangent_neighbours < static_cast<std::size_t>(min_neighbors_for_tangent_axis_))
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{
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//set X axis as a random axis
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x_axis = pcl::geometry::randomOrthogonalAxis (fitted_normal);
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y_axis = fitted_normal.cross (x_axis);
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lrf.row (0).matrix () = x_axis;
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lrf.row (1).matrix () = y_axis;
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return (std::numeric_limits<SignedDistanceT>::max ());
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}
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//find point with the largest signed distance from tangent plane
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SignedDistanceT shape_score;
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SignedDistanceT best_shape_score = -std::numeric_limits<SignedDistanceT>::max ();
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int best_shape_index = -1;
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Eigen::Vector3f best_margin_point;
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const float radius2 = tangent_radius_ * tangent_radius_;
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const float margin_distance2 = margin_thresh_ * margin_thresh_ * radius2;
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Vector3fMapConst feature_point = (*input_)[index].getVector3fMap ();
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for (std::size_t curr_neigh = 0; curr_neigh < n_tangent_neighbours; ++curr_neigh)
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{
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const int& curr_neigh_idx = neighbours_indices[curr_neigh];
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const float& neigh_distance_sqr = neighbours_distances[curr_neigh];
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if (neigh_distance_sqr <= margin_distance2)
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{
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continue;
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}
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//point curr_neigh_idx is inside the ring between marginThresh and Radius
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shape_score = fitted_normal.dot ((*sampled_surface_)[curr_neigh_idx].getVector3fMap ());
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if (shape_score > best_shape_score)
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{
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best_shape_index = curr_neigh_idx;
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best_shape_score = shape_score;
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}
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} //for each neighbor
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if (best_shape_index == -1)
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{
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x_axis = pcl::geometry::randomOrthogonalAxis (fitted_normal);
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y_axis = fitted_normal.cross (x_axis);
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lrf.row (0).matrix () = x_axis;
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lrf.row (1).matrix () = y_axis;
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return (std::numeric_limits<SignedDistanceT>::max ());
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}
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//find orthogonal axis directed to best_shape_index point projection on plane with fittedNormal as axis
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x_axis = pcl::geometry::projectedAsUnitVector (sampled_surface_->at (best_shape_index).getVector3fMap (), feature_point, fitted_normal);
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y_axis = fitted_normal.cross (x_axis);
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lrf.row (0).matrix () = x_axis;
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lrf.row (1).matrix () = y_axis;
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//z axis already set
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best_shape_score -= fitted_normal.dot (feature_point);
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return (best_shape_score);
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}
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//////////////////////////////////////////////////////////////////////////////////////////////
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template<typename PointInT, typename PointNT, typename PointOutT, typename SignedDistanceT> void
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pcl::FLARELocalReferenceFrameEstimation<PointInT, PointNT, PointOutT, SignedDistanceT>::computeFeature (PointCloudOut &output)
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{
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//check whether used with search radius or search k-neighbors
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if (this->getKSearch () != 0)
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{
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PCL_ERROR (
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"[pcl::%s::computeFeature] Error! Search method set to k-neighborhood. Call setKSearch (0) and setRadiusSearch (radius) to use this class.\n",
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getClassName ().c_str ());
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return;
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}
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signed_distances_from_highest_points_.resize (indices_->size ());
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for (std::size_t point_idx = 0; point_idx < indices_->size (); ++point_idx)
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{
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Eigen::Matrix3f currentLrf;
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PointOutT &rf = output[point_idx];
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signed_distances_from_highest_points_[point_idx] = computePointLRF ((*indices_)[point_idx], currentLrf);
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if (signed_distances_from_highest_points_[point_idx] == std::numeric_limits<SignedDistanceT>::max ())
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{
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output.is_dense = false;
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}
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rf.getXAxisVector3fMap () = currentLrf.row (0);
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rf.getYAxisVector3fMap () = currentLrf.row (1);
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rf.getZAxisVector3fMap () = currentLrf.row (2);
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}
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}
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#define PCL_INSTANTIATE_FLARELocalReferenceFrameEstimation(T,NT,OutT,SdT) template class PCL_EXPORTS pcl::FLARELocalReferenceFrameEstimation<T,NT,OutT,SdT>;
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#endif // PCL_FEATURES_IMPL_FLARE_H_
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