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hv_go.hpp
/*
* Software License Agreement (BSD License)
*
* Point Cloud Library (PCL) - www.pointclouds.org
* Copyright (c) 2012 Aitor Aldoma, Federico Tombari
*
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#ifndef PCL_RECOGNITION_IMPL_HV_GO_HPP_
#define PCL_RECOGNITION_IMPL_HV_GO_HPP_
#include <pcl/recognition/hv/hv_go.h>
#include <pcl/common/common.h> // for getMinMax3D
#include <pcl/common/time.h>
#include <pcl/point_types.h>
#include <memory>
#include <numeric>
template<typename PointT, typename NormalT>
inline void extractEuclideanClustersSmooth(const typename pcl::PointCloud<PointT> &cloud, const typename pcl::PointCloud<NormalT> &normals, float tolerance,
const typename pcl::search::Search<PointT>::Ptr &tree, std::vector<pcl::PointIndices> &clusters, double eps_angle, float curvature_threshold,
unsigned int min_pts_per_cluster, unsigned int max_pts_per_cluster = (std::numeric_limits<int>::max) ())
{
if (tree->getInputCloud ()->size () != cloud.size ())
{
PCL_ERROR("[pcl::extractEuclideanClusters] Tree built for a different point cloud dataset\n");
return;
}
if (cloud.size () != normals.size ())
{
PCL_ERROR("[pcl::extractEuclideanClusters] Number of points in the input point cloud different than normals!\n");
return;
}
// If tree gives sorted results, we can skip the first one because it is the query point itself
const std::size_t nn_start_idx = tree->getSortedResults () ? 1 : 0;
// Create a bool vector of processed point indices, and initialize it to false
std::vector<bool> processed (cloud.size (), false);
pcl::Indices nn_indices;
std::vector<float> nn_distances;
// Process all points in the indices vector
int size = static_cast<int> (cloud.size ());
for (int i = 0; i < size; ++i)
{
if (processed[i])
continue;
std::vector<unsigned int> seed_queue;
int sq_idx = 0;
seed_queue.push_back (i);
processed[i] = true;
while (sq_idx < static_cast<int> (seed_queue.size ()))
{
if (normals[seed_queue[sq_idx]].curvature > curvature_threshold)
{
sq_idx++;
continue;
}
// Search for sq_idx
if (!tree->radiusSearch (seed_queue[sq_idx], tolerance, nn_indices, nn_distances))
{
sq_idx++;
continue;
}
for (std::size_t j = nn_start_idx; j < nn_indices.size (); ++j)
{
if (processed[nn_indices[j]]) // Has this point been processed before ?
continue;
if (normals[nn_indices[j]].curvature > curvature_threshold)
{
continue;
}
//processed[nn_indices[j]] = true;
// [-1;1]
double dot_p = normals[seed_queue[sq_idx]].normal[0] * normals[nn_indices[j]].normal[0]
+ normals[seed_queue[sq_idx]].normal[1] * normals[nn_indices[j]].normal[1]
+ normals[seed_queue[sq_idx]].normal[2] * normals[nn_indices[j]].normal[2];
if (std::abs (std::acos (dot_p)) < eps_angle)
{
processed[nn_indices[j]] = true;
seed_queue.push_back (nn_indices[j]);
}
}
sq_idx++;
}
// If this queue is satisfactory, add to the clusters
if (seed_queue.size () >= min_pts_per_cluster && seed_queue.size () <= max_pts_per_cluster)
{
pcl::PointIndices r;
r.indices.resize (seed_queue.size ());
for (std::size_t j = 0; j < seed_queue.size (); ++j)
r.indices[j] = seed_queue[j];
std::sort (r.indices.begin (), r.indices.end ());
r.indices.erase (std::unique (r.indices.begin (), r.indices.end ()), r.indices.end ());
r.header = cloud.header;
clusters.push_back (r); // We could avoid a copy by working directly in the vector
}
}
}
template<typename ModelT, typename SceneT>
mets::gol_type pcl::GlobalHypothesesVerification<ModelT, SceneT>::evaluateSolution(const std::vector<bool> & active, int changed)
{
float sign = 1.f;
//update explained_by_RM
if (active[changed])
{
//it has been activated
updateExplainedVector (recognition_models_[changed]->explained_, recognition_models_[changed]->explained_distances_, explained_by_RM_,
explained_by_RM_distance_weighted, 1.f);
updateUnexplainedVector (recognition_models_[changed]->unexplained_in_neighborhood, recognition_models_[changed]->unexplained_in_neighborhood_weights,
unexplained_by_RM_neighboorhods, recognition_models_[changed]->explained_, explained_by_RM_, 1.f);
updateCMDuplicity(recognition_models_[changed]->complete_cloud_occupancy_indices_, complete_cloud_occupancy_by_RM_, 1.f);
} else
{
//it has been deactivated
updateExplainedVector (recognition_models_[changed]->explained_, recognition_models_[changed]->explained_distances_, explained_by_RM_,
explained_by_RM_distance_weighted, -1.f);
updateUnexplainedVector (recognition_models_[changed]->unexplained_in_neighborhood, recognition_models_[changed]->unexplained_in_neighborhood_weights,
unexplained_by_RM_neighboorhods, recognition_models_[changed]->explained_, explained_by_RM_, -1.f);
updateCMDuplicity(recognition_models_[changed]->complete_cloud_occupancy_indices_, complete_cloud_occupancy_by_RM_, -1.f);
sign = -1.f;
}
int duplicity = getDuplicity ();
float good_info = getExplainedValue ();
float unexplained_info = getPreviousUnexplainedValue ();
float bad_info = static_cast<float> (getPreviousBadInfo ())
+ (recognition_models_[changed]->outliers_weight_ * static_cast<float> (recognition_models_[changed]->bad_information_)) * sign;
setPreviousBadInfo (bad_info);
int n_active_hyp = 0;
for(const bool i : active) {
if(i)
n_active_hyp++;
}
float duplicity_cm = static_cast<float> (getDuplicityCM ()) * w_occupied_multiple_cm_;
return static_cast<mets::gol_type> ((good_info - bad_info - static_cast<float> (duplicity) - unexplained_info - duplicity_cm - static_cast<float> (n_active_hyp)) * -1.f); //return the dual to our max problem
}
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void pcl::GlobalHypothesesVerification<ModelT, SceneT>::initialize()
{
//clear stuff
recognition_models_.clear ();
unexplained_by_RM_neighboorhods.clear ();
explained_by_RM_distance_weighted.clear ();
explained_by_RM_.clear ();
mask_.clear ();
indices_.clear (),
complete_cloud_occupancy_by_RM_.clear ();
// initialize mask to false
mask_.resize (complete_models_.size ());
for (std::size_t i = 0; i < complete_models_.size (); i++)
mask_[i] = false;
indices_.resize (complete_models_.size ());
NormalEstimator_ n3d;
scene_normals_.reset (new pcl::PointCloud<pcl::Normal> ());
typename pcl::search::KdTree<SceneT>::Ptr normals_tree (new pcl::search::KdTree<SceneT>);
normals_tree->setInputCloud (scene_cloud_downsampled_);
n3d.setRadiusSearch (radius_normals_);
n3d.setSearchMethod (normals_tree);
n3d.setInputCloud (scene_cloud_downsampled_);
n3d.compute (*scene_normals_);
//check nans...
int j = 0;
for (std::size_t i = 0; i < scene_normals_->size (); ++i)
{
if (!std::isfinite ((*scene_normals_)[i].normal_x) || !std::isfinite ((*scene_normals_)[i].normal_y)
|| !std::isfinite ((*scene_normals_)[i].normal_z))
continue;
(*scene_normals_)[j] = (*scene_normals_)[i];
(*scene_cloud_downsampled_)[j] = (*scene_cloud_downsampled_)[i];
j++;
}
scene_normals_->points.resize (j);
scene_normals_->width = j;
scene_normals_->height = 1;
scene_cloud_downsampled_->points.resize (j);
scene_cloud_downsampled_->width = j;
scene_cloud_downsampled_->height = 1;
explained_by_RM_.resize (scene_cloud_downsampled_->size (), 0);
explained_by_RM_distance_weighted.resize (scene_cloud_downsampled_->size (), 0.f);
unexplained_by_RM_neighboorhods.resize (scene_cloud_downsampled_->size (), 0.f);
//compute segmentation of the scene if detect_clutter_
if (detect_clutter_)
{
//initialize kdtree for search
scene_downsampled_tree_.reset (new pcl::search::KdTree<SceneT>);
scene_downsampled_tree_->setInputCloud (scene_cloud_downsampled_);
std::vector<pcl::PointIndices> clusters;
double eps_angle_threshold = 0.2;
int min_points = 20;
float curvature_threshold = 0.045f;
extractEuclideanClustersSmooth<SceneT, pcl::Normal> (*scene_cloud_downsampled_, *scene_normals_, inliers_threshold_ * 2.f, scene_downsampled_tree_,
clusters, eps_angle_threshold, curvature_threshold, min_points);
clusters_cloud_.reset (new pcl::PointCloud<pcl::PointXYZI>);
clusters_cloud_->points.resize (scene_cloud_downsampled_->size ());
clusters_cloud_->width = scene_cloud_downsampled_->width;
clusters_cloud_->height = 1;
for (std::size_t i = 0; i < scene_cloud_downsampled_->size (); i++)
{
pcl::PointXYZI p;
p.getVector3fMap () = (*scene_cloud_downsampled_)[i].getVector3fMap ();
p.intensity = 0.f;
(*clusters_cloud_)[i] = p;
}
float intens_incr = 100.f / static_cast<float> (clusters.size ());
float intens = intens_incr;
for (const auto &cluster : clusters)
{
for (const auto &vertex : cluster.indices)
{
(*clusters_cloud_)[vertex].intensity = intens;
}
intens += intens_incr;
}
}
//compute cues
{
pcl::ScopeTime tcues ("Computing cues");
recognition_models_.resize (complete_models_.size ());
int valid = 0;
for (int i = 0; i < static_cast<int> (complete_models_.size ()); i++)
{
//create recognition model
recognition_models_[valid].reset (new RecognitionModel ());
if(addModel (visible_models_[i], complete_models_[i], recognition_models_[valid])) {
indices_[valid] = i;
valid++;
}
}
recognition_models_.resize(valid);
indices_.resize(valid);
}
//compute the bounding boxes for the models
ModelT min_pt_all, max_pt_all;
min_pt_all.x = min_pt_all.y = min_pt_all.z = std::numeric_limits<float>::max ();
max_pt_all.x = max_pt_all.y = max_pt_all.z = (std::numeric_limits<float>::max () - 0.001f) * -1;
for (std::size_t i = 0; i < recognition_models_.size (); i++)
{
ModelT min_pt, max_pt;
pcl::getMinMax3D (*complete_models_[indices_[i]], min_pt, max_pt);
if (min_pt.x < min_pt_all.x)
min_pt_all.x = min_pt.x;
if (min_pt.y < min_pt_all.y)
min_pt_all.y = min_pt.y;
if (min_pt.z < min_pt_all.z)
min_pt_all.z = min_pt.z;
if (max_pt.x > max_pt_all.x)
max_pt_all.x = max_pt.x;
if (max_pt.y > max_pt_all.y)
max_pt_all.y = max_pt.y;
if (max_pt.z > max_pt_all.z)
max_pt_all.z = max_pt.z;
}
int size_x, size_y, size_z;
size_x = static_cast<int> (std::ceil (std::abs (max_pt_all.x - min_pt_all.x) / res_occupancy_grid_)) + 1;
size_y = static_cast<int> (std::ceil (std::abs (max_pt_all.y - min_pt_all.y) / res_occupancy_grid_)) + 1;
size_z = static_cast<int> (std::ceil (std::abs (max_pt_all.z - min_pt_all.z) / res_occupancy_grid_)) + 1;
complete_cloud_occupancy_by_RM_.resize (size_x * size_y * size_z, 0);
for (std::size_t i = 0; i < recognition_models_.size (); i++)
{
std::map<int, bool> banned;
std::map<int, bool>::iterator banned_it;
for (const auto& point: *complete_models_[indices_[i]])
{
const int pos_x = static_cast<int> (std::floor ((point.x - min_pt_all.x) / res_occupancy_grid_));
const int pos_y = static_cast<int> (std::floor ((point.y - min_pt_all.y) / res_occupancy_grid_));
const int pos_z = static_cast<int> (std::floor ((point.z - min_pt_all.z) / res_occupancy_grid_));
const int idx = pos_z * size_x * size_y + pos_y * size_x + pos_x;
banned_it = banned.find (idx);
if (banned_it == banned.end ())
{
complete_cloud_occupancy_by_RM_[idx]++;
recognition_models_[i]->complete_cloud_occupancy_indices_.push_back (idx);
banned[idx] = true;
}
}
}
{
pcl::ScopeTime tcues ("Computing clutter cues");
#pragma omp parallel for \
default(none) \
schedule(dynamic, 4) \
num_threads(omp_get_num_procs())
for (int j = 0; j < static_cast<int> (recognition_models_.size ()); j++)
computeClutterCue (recognition_models_[j]);
}
cc_.clear ();
n_cc_ = 1;
cc_.resize (n_cc_);
for (std::size_t i = 0; i < recognition_models_.size (); i++)
cc_[0].push_back (static_cast<int> (i));
}
template<typename ModelT, typename SceneT>
void pcl::GlobalHypothesesVerification<ModelT, SceneT>::SAOptimize(std::vector<int> & cc_indices, std::vector<bool> & initial_solution)
{
//temporal copy of recogniton_models_
std::vector<RecognitionModelPtr> recognition_models_copy;
recognition_models_copy = recognition_models_;
recognition_models_.clear ();
for (const int &cc_index : cc_indices)
{
recognition_models_.push_back (recognition_models_copy[cc_index]);
}
for (std::size_t j = 0; j < recognition_models_.size (); j++)
{
RecognitionModelPtr recog_model = recognition_models_[j];
for (std::size_t i = 0; i < recog_model->explained_.size (); i++)
{
explained_by_RM_[recog_model->explained_[i]]++;
explained_by_RM_distance_weighted[recog_model->explained_[i]] += recog_model->explained_distances_[i];
}
if (detect_clutter_)
{
for (std::size_t i = 0; i < recog_model->unexplained_in_neighborhood.size (); i++)
{
unexplained_by_RM_neighboorhods[recog_model->unexplained_in_neighborhood[i]] += recog_model->unexplained_in_neighborhood_weights[i];
}
}
}
int occupied_multiple = 0;
for(const auto& i : complete_cloud_occupancy_by_RM_) {
if(i > 1) {
occupied_multiple+=i;
}
}
setPreviousDuplicityCM(occupied_multiple);
//do optimization
//Define model SAModel, initial solution is all models activated
int duplicity;
float good_information_ = getTotalExplainedInformation (explained_by_RM_, explained_by_RM_distance_weighted, &duplicity);
float bad_information_ = 0;
float unexplained_in_neighboorhod = getUnexplainedInformationInNeighborhood (unexplained_by_RM_neighboorhods, explained_by_RM_);
for (std::size_t i = 0; i < initial_solution.size (); i++)
{
if (initial_solution[i])
bad_information_ += recognition_models_[i]->outliers_weight_ * static_cast<float> (recognition_models_[i]->bad_information_);
}
setPreviousExplainedValue (good_information_);
setPreviousDuplicity (duplicity);
setPreviousBadInfo (bad_information_);
setPreviousUnexplainedValue (unexplained_in_neighboorhod);
SAModel model;
model.cost_ = static_cast<mets::gol_type> ((good_information_ - bad_information_
- static_cast<float> (duplicity)
- static_cast<float> (occupied_multiple) * w_occupied_multiple_cm_
- static_cast<float> (recognition_models_.size ())
- unexplained_in_neighboorhod) * -1.f);
model.setSolution (initial_solution);
model.setOptimizer (this);
SAModel best (model);
move_manager neigh (static_cast<int> (cc_indices.size ()));
mets::best_ever_solution best_recorder (best);
mets::noimprove_termination_criteria noimprove (max_iterations_);
mets::linear_cooling linear_cooling;
mets::simulated_annealing<move_manager> sa (model, best_recorder, neigh, noimprove, linear_cooling, initial_temp_, 1e-7, 2);
sa.setApplyAndEvaluate(true);
{
pcl::ScopeTime t ("SA search...");
sa.search ();
}
best_seen_ = static_cast<const SAModel&> (best_recorder.best_seen ());
for (std::size_t i = 0; i < best_seen_.solution_.size (); i++)
{
initial_solution[i] = best_seen_.solution_[i];
}
recognition_models_ = recognition_models_copy;
}
///////////////////////////////////////////////////////////////////////////////////////////////////
template<typename ModelT, typename SceneT>
void pcl::GlobalHypothesesVerification<ModelT, SceneT>::verify()
{
initialize ();
//for each connected component, find the optimal solution
for (int c = 0; c < n_cc_; c++)
{
//TODO: Check for trivial case...
//TODO: Check also the number of hypotheses and use exhaustive enumeration if smaller than 10
std::vector<bool> subsolution (cc_[c].size (), true);
SAOptimize (cc_[c], subsolution);
for (std::size_t i = 0; i < subsolution.size (); i++)
{
mask_[indices_[cc_[c][i]]] = (subsolution[i]);
}
}
}
template<typename ModelT, typename SceneT>
bool pcl::GlobalHypothesesVerification<ModelT, SceneT>::addModel(typename pcl::PointCloud<ModelT>::ConstPtr & model,
typename pcl::PointCloud<ModelT>::ConstPtr & complete_model, RecognitionModelPtr & recog_model)
{
//voxelize model cloud
recog_model->cloud_.reset (new pcl::PointCloud<ModelT> ());
recog_model->complete_cloud_.reset (new pcl::PointCloud<ModelT> ());
float size_model = resolution_;
pcl::VoxelGrid<ModelT> voxel_grid;
voxel_grid.setInputCloud (model);
voxel_grid.setLeafSize (size_model, size_model, size_model);
voxel_grid.filter (*(recog_model->cloud_));
pcl::VoxelGrid<ModelT> voxel_grid2;
voxel_grid2.setInputCloud (complete_model);
voxel_grid2.setLeafSize (size_model, size_model, size_model);
voxel_grid2.filter (*(recog_model->complete_cloud_));
{
//check nans...
int j = 0;
for (auto& point: *(recog_model->cloud_))
{
if (!isXYZFinite (point))
continue;
(*recog_model->cloud_)[j] = point;
j++;
}
recog_model->cloud_->points.resize (j);
recog_model->cloud_->width = j;
recog_model->cloud_->height = 1;
}
if (recog_model->cloud_->points.empty ())
{
PCL_WARN("The model cloud has no points..\n");
return false;
}
//compute normals unless given (now do it always...)
typename pcl::search::KdTree<ModelT>::Ptr normals_tree (new pcl::search::KdTree<ModelT>);
pcl::NormalEstimation<ModelT, pcl::Normal> n3d;
recog_model->normals_.reset (new pcl::PointCloud<pcl::Normal> ());
normals_tree->setInputCloud (recog_model->cloud_);
n3d.setRadiusSearch (radius_normals_);
n3d.setSearchMethod (normals_tree);
n3d.setInputCloud ((recog_model->cloud_));
n3d.compute (*(recog_model->normals_));
//check nans...
int j = 0;
for (std::size_t i = 0; i < recog_model->normals_->size (); ++i)
{
if (isNormalFinite((*recog_model->normals_)[i]))
continue;
(*recog_model->normals_)[j] = (*recog_model->normals_)[i];
(*recog_model->cloud_)[j] = (*recog_model->cloud_)[i];
j++;
}
recog_model->normals_->points.resize (j);
recog_model->normals_->width = j;
recog_model->normals_->height = 1;
recog_model->cloud_->points.resize (j);
recog_model->cloud_->width = j;
recog_model->cloud_->height = 1;
std::vector<int> explained_indices;
std::vector<float> outliers_weight;
std::vector<float> explained_indices_distances;
pcl::Indices nn_indices;
std::vector<float> nn_distances;
std::map<int, std::shared_ptr<std::vector<std::pair<int, float>>>> model_explains_scene_points; //which point i from the scene is explained by a points j_k with dist d_k from the model
outliers_weight.resize (recog_model->cloud_->size ());
recog_model->outlier_indices_.resize (recog_model->cloud_->size ());
std::size_t o = 0;
for (std::size_t i = 0; i < recog_model->cloud_->size (); i++)
{
if (!scene_downsampled_tree_->radiusSearch ((*recog_model->cloud_)[i], inliers_threshold_, nn_indices, nn_distances, std::numeric_limits<int>::max ()))
{
//outlier
outliers_weight[o] = regularizer_;
recog_model->outlier_indices_[o] = static_cast<int> (i);
o++;
} else
{
for (std::size_t k = 0; k < nn_distances.size (); k++)
{
std::pair<int, float> pair = std::make_pair (i, nn_distances[k]); //i is a index to a model point and then distance
auto it = model_explains_scene_points.find (nn_indices[k]);
if (it == model_explains_scene_points.end ())
{
std::shared_ptr<std::vector<std::pair<int, float>>> vec (new std::vector<std::pair<int, float>> ());
vec->push_back (pair);
model_explains_scene_points[nn_indices[k]] = vec;
} else
{
it->second->push_back (pair);
}
}
}
}
outliers_weight.resize (o);
recog_model->outlier_indices_.resize (o);
recog_model->outliers_weight_ = (std::accumulate (outliers_weight.begin (), outliers_weight.end (), 0.f) / static_cast<float> (outliers_weight.size ()));
if (outliers_weight.empty ())
recog_model->outliers_weight_ = 1.f;
pcl::IndicesPtr indices_scene (new pcl::Indices);
//go through the map and keep the closest model point in case that several model points explain a scene point
int p = 0;
for (auto it = model_explains_scene_points.cbegin (); it != model_explains_scene_points.cend (); it++, p++)
{
std::size_t closest = 0;
float min_d = std::numeric_limits<float>::min ();
for (std::size_t i = 0; i < it->second->size (); i++)
{
if (it->second->at (i).second > min_d)
{
min_d = it->second->at (i).second;
closest = i;
}
}
float d = it->second->at (closest).second;
float d_weight = -(d * d / (inliers_threshold_)) + 1;
//it->first is index to scene point
//using normals to weight inliers
Eigen::Vector3f scene_p_normal = (*scene_normals_)[it->first].getNormalVector3fMap ();
Eigen::Vector3f model_p_normal =
(*recog_model->normals_)[it->second->at(closest).first].getNormalVector3fMap();
float dotp = scene_p_normal.dot (model_p_normal) * 1.f; //[-1,1] from antiparallel through perpendicular to parallel
if (dotp < 0.f)
dotp = 0.f;
explained_indices.push_back (it->first);
explained_indices_distances.push_back (d_weight * dotp);
}
recog_model->bad_information_ = static_cast<int> (recog_model->outlier_indices_.size ());
recog_model->explained_ = explained_indices;
recog_model->explained_distances_ = explained_indices_distances;
return true;
}
template<typename ModelT, typename SceneT>
void pcl::GlobalHypothesesVerification<ModelT, SceneT>::computeClutterCue(RecognitionModelPtr & recog_model)
{
if (detect_clutter_)
{
float rn_sqr = radius_neighborhood_GO_ * radius_neighborhood_GO_;
pcl::Indices nn_indices;
std::vector<float> nn_distances;
std::vector < std::pair<int, int> > neighborhood_indices; //first is indices to scene point and second is indices to explained_ scene points
for (pcl::index_t i = 0; i < static_cast<pcl::index_t> (recog_model->explained_.size ()); i++)
{
if (scene_downsampled_tree_->radiusSearch ((*scene_cloud_downsampled_)[recog_model->explained_[i]], radius_neighborhood_GO_, nn_indices,
nn_distances, std::numeric_limits<int>::max ()))
{
for (std::size_t k = 0; k < nn_distances.size (); k++)
{
if (nn_indices[k] != i)
neighborhood_indices.emplace_back (nn_indices[k], i);
}
}
}
//sort neighborhood indices by id
std::sort (neighborhood_indices.begin (), neighborhood_indices.end (),
[] (const auto& p1, const auto& p2) { return p1.first < p2.first; });
//erase duplicated unexplained points
neighborhood_indices.erase (
std::unique (neighborhood_indices.begin (), neighborhood_indices.end (),
[] (const auto& p1, const auto& p2) { return p1.first == p2.first; }), neighborhood_indices.end ());
//sort explained points
std::vector<int> exp_idces (recog_model->explained_);
std::sort (exp_idces.begin (), exp_idces.end ());
recog_model->unexplained_in_neighborhood.resize (neighborhood_indices.size ());
recog_model->unexplained_in_neighborhood_weights.resize (neighborhood_indices.size ());
std::size_t p = 0;
std::size_t j = 0;
for (const auto &neighborhood_index : neighborhood_indices)
{
if ((j < exp_idces.size ()) && (neighborhood_index.first == exp_idces[j]))
{
//this index is explained by the hypothesis so ignore it, advance j
j++;
} else
{
//indices_in_nb[i] < exp_idces[j]
//recog_model->unexplained_in_neighborhood.push_back(neighborhood_indices[i]);
recog_model->unexplained_in_neighborhood[p] = neighborhood_index.first;
if ((*clusters_cloud_)[recog_model->explained_[neighborhood_index.second]].intensity != 0.f
&& ((*clusters_cloud_)[recog_model->explained_[neighborhood_index.second]].intensity
== (*clusters_cloud_)[neighborhood_index.first].intensity))
{
recog_model->unexplained_in_neighborhood_weights[p] = clutter_regularizer_;
} else
{
//neighborhood_indices[i].first gives the index to the scene point and second to the explained scene point by the model causing this...
//calculate weight of this clutter point based on the distance of the scene point and the model point causing it
float d = static_cast<float> (pow (
((*scene_cloud_downsampled_)[recog_model->explained_[neighborhood_index.second]].getVector3fMap ()
- (*scene_cloud_downsampled_)[neighborhood_index.first].getVector3fMap ()).norm (), 2));
float d_weight = -(d / rn_sqr) + 1; //points that are close have a strong weight*/
//using normals to weight clutter points
Eigen::Vector3f scene_p_normal = (*scene_normals_)[neighborhood_index.first].getNormalVector3fMap ();
Eigen::Vector3f model_p_normal = (*scene_normals_)[recog_model->explained_[neighborhood_index.second]].getNormalVector3fMap ();
float dotp = scene_p_normal.dot (model_p_normal); //[-1,1] from antiparallel through perpendicular to parallel
if (dotp < 0)
dotp = 0.f;
recog_model->unexplained_in_neighborhood_weights[p] = d_weight * dotp;
}
p++;
}
}
recog_model->unexplained_in_neighborhood_weights.resize (p);
recog_model->unexplained_in_neighborhood.resize (p);
}
}
#define PCL_INSTANTIATE_GoHV(T1,T2) template class PCL_EXPORTS pcl::GlobalHypothesesVerification<T1,T2>;
#endif /* PCL_RECOGNITION_IMPL_HV_GO_HPP_ */
pcl::Feature::setRadiusSearch
void setRadiusSearch(double radius)
Set the sphere radius that is to be used for determining the nearest neighbors used for the feature e...
Definition feature.h:198
pcl::Feature::setSearchMethod
void setSearchMethod(const KdTreePtr &tree)
Provide a pointer to the search object.
Definition feature.h:164
pcl::Feature::compute
void compute(PointCloudOut &output)
Base method for feature estimation for all points given in <setInputCloud (), setIndices ()> using th...
Definition feature.hpp:194
pcl::Filter::filter
void filter(PointCloud &output)
Calls the filtering method and returns the filtered dataset in output.
Definition filter.h:121
pcl::GlobalHypothesesVerification
A hypothesis verification method proposed in "A Global Hypotheses Verification Method for 3D Object R...
Definition hv_go.h:37
pcl::GlobalHypothesesVerification::verify
void verify() override
Definition hv_go.hpp:477
pcl::NormalEstimation
NormalEstimation estimates local surface properties (surface normals and curvatures)at each 3D point.
Definition normal_3d.h:244
pcl::NormalEstimation::setInputCloud
void setInputCloud(const PointCloudConstPtr &cloud) override
Provide a pointer to the input dataset.
Definition normal_3d.h:328
pcl::PCLBase::setInputCloud
virtual void setInputCloud(const PointCloudConstPtr &cloud)
Provide a pointer to the input dataset.
Definition pcl_base.hpp:65
pcl::PointCloud
PointCloud represents the base class in PCL for storing collections of 3D points.
Definition point_cloud.h:173
pcl::PointCloud::push_back
void push_back(const PointT &pt)
Insert a new point in the cloud, at the end of the container.
Definition point_cloud.h:663
pcl::PointCloud::header
pcl::PCLHeader header
The point cloud header.
Definition point_cloud.h:392
pcl::PointCloud::size
std::size_t size() const
Definition point_cloud.h:443
pcl::PointCloud::ConstPtr
shared_ptr< const PointCloud< PointT > > ConstPtr
Definition point_cloud.h:414
pcl::ScopeTime
Class to measure the time spent in a scope.
Definition time.h:106
pcl::VoxelGrid
VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data.
Definition voxel_grid.h:210
pcl::VoxelGrid::setLeafSize
void setLeafSize(const Eigen::Vector4f &leaf_size)
Set the voxel grid leaf size.
Definition voxel_grid.h:247
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::KdTree::Ptr
shared_ptr< KdTree< PointT, Tree > > Ptr
Definition kdtree.h:75
pcl::search::Search::getSortedResults
virtual bool getSortedResults()
Gets whether the results should be sorted (ascending in the distance) or not Otherwise the results ma...
Definition search.hpp:68
pcl::search::Search::Ptr
shared_ptr< pcl::search::Search< PointT > > Ptr
Definition search.h:81
pcl::search::Search::getInputCloud
virtual PointCloudConstPtr getInputCloud() const
Get a pointer to the input point cloud dataset.
Definition search.h:124
pcl::search::Search::radiusSearch
virtual int radiusSearch(const PointT &point, double radius, Indices &k_indices, std::vector< float > &k_sqr_distances, unsigned int max_nn=0) const =0
Search for all the nearest neighbors of the query point in a given radius.
common.h
Define standard C methods and C++ classes that are common to all methods.
point_types.h
Defines all the PCL implemented PointT point type structures.
time.h
Define methods for measuring time spent in code blocks.
pcl::getMinMax3D
void getMinMax3D(const pcl::PointCloud< PointT > &cloud, PointT &min_pt, PointT &max_pt)
Get the minimum and maximum values on each of the 3 (x-y-z) dimensions in a given pointcloud.
Definition common.hpp:295
pcl::index_t
detail::int_type_t< detail::index_type_size, detail::index_type_signed > index_t
Type used for an index in PCL.
Definition types.h:112
pcl::Indices
IndicesAllocator<> Indices
Type used for indices in PCL.
Definition types.h:133
pcl::IndicesPtr
shared_ptr< Indices > IndicesPtr
Definition pcl_base.h:58
pcl::isNormalFinite
constexpr bool isNormalFinite(const PointT &) noexcept
Definition point_tests.h:131
pcl::_PointXYZI::intensity
float intensity
Definition point_types.hpp:448
pcl::PointIndices
Definition PointIndices.h:12
pcl::PointIndices::header
::pcl::PCLHeader header
Definition PointIndices.h:18
pcl::PointIndices::indices
Indices indices
Definition PointIndices.h:20
pcl::PointXYZI
Definition point_types.hpp:457