int
main (int argc, char *argv[])
{
parseCommandLine (argc, argv);
pcl::PointCloud<PointType>::Ptr model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr model_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr scene_keypoints (new pcl::PointCloud<PointType> ());
pcl::PointCloud<NormalType>::Ptr model_normals (new pcl::PointCloud<NormalType> ());
pcl::PointCloud<NormalType>::Ptr scene_normals (new pcl::PointCloud<NormalType> ());
pcl::PointCloud<DescriptorType>::Ptr model_descriptors (new pcl::PointCloud<DescriptorType> ());
pcl::PointCloud<DescriptorType>::Ptr scene_descriptors (new pcl::PointCloud<DescriptorType> ());
//
// Load clouds
//
if (pcl::io::loadPCDFile (model_filename_, *model) < 0)
{
std::cout << "Error loading model cloud." << std::endl;
showHelp (argv[0]);
return (-1);
}
if (pcl::io::loadPCDFile (scene_filename_, *scene) < 0)
{
std::cout << "Error loading scene cloud." << std::endl;
showHelp (argv[0]);
return (-1);
}
//
// Set up resolution invariance
//
if (use_cloud_resolution_)
{
float resolution = static_cast<float> (computeCloudResolution (model));
if (resolution != 0.0f)
{
model_ss_ *= resolution;
scene_ss_ *= resolution;
rf_rad_ *= resolution;
descr_rad_ *= resolution;
cg_size_ *= resolution;
}
std::cout << "Model resolution: " << resolution << std::endl;
std::cout << "Model sampling size: " << model_ss_ << std::endl;
std::cout << "Scene sampling size: " << scene_ss_ << std::endl;
std::cout << "LRF support radius: " << rf_rad_ << std::endl;
std::cout << "SHOT descriptor radius: " << descr_rad_ << std::endl;
std::cout << "Clustering bin size: " << cg_size_ << std::endl << std::endl;
}
//
// Compute Normals
//
pcl::NormalEstimationOMP<PointType, NormalType> norm_est;
norm_est.setKSearch (10);
norm_est.setInputCloud (model);
norm_est.compute (*model_normals);
norm_est.setInputCloud (scene);
norm_est.compute (*scene_normals);
//
// Downsample Clouds to Extract keypoints
//
/*
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (model);
uniform_sampling.setRadiusSearch (model_ss_);
uniform_sampling.filter (*model_keypoints);
std::cout << "Model total points: " << model->size () << "; Selected Keypoints: " << model_keypoints->size () << std::endl;
uniform_sampling.setInputCloud (scene);
uniform_sampling.setRadiusSearch (scene_ss_);
uniform_sampling.filter (*scene_keypoints);
std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;
*/
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (model);
uniform_sampling.setRadiusSearch (model_ss_);
//uniform_sampling.filter (*model_keypoints);
pcl::PointCloud<int> keypointIndices1;
uniform_sampling.compute(keypointIndices1);
pcl::copyPointCloud(*model, keypointIndices1.points, *model_keypoints);
std::cout << "Model total points: " << model->size () << "; Selected Keypoints: " << model_keypoints->size () << std::endl;
uniform_sampling.setInputCloud (scene);
uniform_sampling.setRadiusSearch (scene_ss_);
//uniform_sampling.filter (*scene_keypoints);
pcl::PointCloud<int> keypointIndices2;
uniform_sampling.compute(keypointIndices2);
pcl::copyPointCloud(*scene, keypointIndices2.points, *scene_keypoints);
std::cout << "Scene total points: " << scene->size () << "; Selected Keypoints: " << scene_keypoints->size () << std::endl;
//
// Compute Descriptor for keypoints
//
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
descr_est.setRadiusSearch (descr_rad_);
descr_est.setInputCloud (model_keypoints);
descr_est.setInputNormals (model_normals);
descr_est.setSearchSurface (model);
descr_est.compute (*model_descriptors);
descr_est.setInputCloud (scene_keypoints);
descr_est.setInputNormals (scene_normals);
descr_est.setSearchSurface (scene);
descr_est.compute (*scene_descriptors);
//
// Find Model-Scene Correspondences with KdTree
//
pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());
pcl::KdTreeFLANN<DescriptorType> match_search;
match_search.setInputCloud (model_descriptors);
// For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.
for (size_t i = 0; i < scene_descriptors->size (); ++i)
{
std::vector<int> neigh_indices (1);
std::vector<float> neigh_sqr_dists (1);
if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) //skipping NaNs
{
continue;
}
int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
if(found_neighs == 1 && neigh_sqr_dists[0] < 0.25f) // add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
{
pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
model_scene_corrs->push_back (corr);
}
}
std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
//
// Actual Clustering
//
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector<pcl::Correspondences> clustered_corrs;
// Using Hough3D
if (use_hough_)
{
//
// Compute (Keypoints) Reference Frames only for Hough
//
pcl::PointCloud<RFType>::Ptr model_rf (new pcl::PointCloud<RFType> ());
pcl::PointCloud<RFType>::Ptr scene_rf (new pcl::PointCloud<RFType> ());
pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
rf_est.setFindHoles (true);
rf_est.setRadiusSearch (rf_rad_);
rf_est.setInputCloud (model_keypoints);
rf_est.setInputNormals (model_normals);
rf_est.setSearchSurface (model);
rf_est.compute (*model_rf);
rf_est.setInputCloud (scene_keypoints);
rf_est.setInputNormals (scene_normals);
rf_est.setSearchSurface (scene);
rf_est.compute (*scene_rf);
// Clustering
pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
clusterer.setHoughBinSize (cg_size_);
clusterer.setHoughThreshold (cg_thresh_);
clusterer.setUseInterpolation (true);
clusterer.setUseDistanceWeight (false);
clusterer.setInputCloud (model_keypoints);
clusterer.setInputRf (model_rf);
clusterer.setSceneCloud (scene_keypoints);
clusterer.setSceneRf (scene_rf);
clusterer.setModelSceneCorrespondences (model_scene_corrs);
//clusterer.cluster (clustered_corrs);
clusterer.recognize (rototranslations, clustered_corrs);
}
else // Using GeometricConsistency
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize (cg_size_);
gc_clusterer.setGCThreshold (cg_thresh_);
gc_clusterer.setInputCloud (model_keypoints);
gc_clusterer.setSceneCloud (scene_keypoints);
gc_clusterer.setModelSceneCorrespondences (model_scene_corrs);
//gc_clusterer.cluster (clustered_corrs);
gc_clusterer.recognize (rototranslations, clustered_corrs);
}
//
// Output results
//
std::cout << "Model instances found: " << rototranslations.size () << std::endl;
for (size_t i = 0; i < rototranslations.size (); ++i)
{
std::cout << "\n Instance " << i + 1 << ":" << std::endl;
std::cout << " Correspondences belonging to this instance: " << clustered_corrs[i].size () << std::endl;
// Print the rotation matrix and translation vector
Eigen::Matrix3f rotation = rototranslations[i].block<3,3>(0, 0);
Eigen::Vector3f translation = rototranslations[i].block<3,1>(0, 3);
printf ("\n");
printf (" | %6.3f %6.3f %6.3f | \n", rotation (0,0), rotation (0,1), rotation (0,2));
printf (" R = | %6.3f %6.3f %6.3f | \n", rotation (1,0), rotation (1,1), rotation (1,2));
printf (" | %6.3f %6.3f %6.3f | \n", rotation (2,0), rotation (2,1), rotation (2,2));
printf ("\n");
printf (" t = < %0.3f, %0.3f, %0.3f >\n", translation (0), translation (1), translation (2));
}
//
// Visualization
//
pcl::visualization::PCLVisualizer viewer ("Correspondence Grouping");
viewer.addPointCloud (scene, "scene_cloud");
pcl::PointCloud<PointType>::Ptr off_scene_model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints (new pcl::PointCloud<PointType> ());
if (show_correspondences_ || show_keypoints_)
{
// We are translating the model so that it doesn't end in the middle of the scene representation
pcl::transformPointCloud (*model, *off_scene_model, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, 255, 255, 128);
viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
}
if (show_keypoints_)
{
pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_keypoints_color_handler (scene_keypoints, 0, 0, 255);
viewer.addPointCloud (scene_keypoints, scene_keypoints_color_handler, "scene_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "scene_keypoints");
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_keypoints_color_handler (off_scene_model_keypoints, 0, 0, 255);
viewer.addPointCloud (off_scene_model_keypoints, off_scene_model_keypoints_color_handler, "off_scene_model_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "off_scene_model_keypoints");
}
for (size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::PointCloud<PointType>::Ptr rotated_model (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*model, *rotated_model, rototranslations[i]);
std::stringstream ss_cloud;
ss_cloud << "instance" << i;
pcl::visualization::PointCloudColorHandlerCustom<PointType> rotated_model_color_handler (rotated_model, 255, 0, 0);
viewer.addPointCloud (rotated_model, rotated_model_color_handler, ss_cloud.str ());
if (show_correspondences_)
{
for (size_t j = 0; j < clustered_corrs[i].size (); ++j)
{
std::stringstream ss_line;
ss_line << "correspondence_line" << i << "_" << j;
PointType& model_point = off_scene_model_keypoints->at (clustered_corrs[i][j].index_query);
PointType& scene_point = scene_keypoints->at (clustered_corrs[i][j].index_match);
// We are drawing a line for each pair of clustered correspondences found between the model and the scene
viewer.addLine<PointType, PointType> (model_point, scene_point, 0, 255, 0, ss_line.str ());
}
}
}
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
return (0);
}
void callback(const PCMsg::ConstPtr& kinect_pc_msg){
// Conversion from sensor_msgs::PointCloud2 to pcl::PointCloud
pcl::fromROSMsg(*kinect_pc_msg, *kinects_pc_);
// Remove NaNs
vector<int> indices;
pcl::removeNaNFromPointCloud<PointType>(*kinects_pc_, *kinects_pc_, indices);
// Downsampling
pc_downsampling<PointType>(kinects_pc_, 0.005,cluster_cloud_);
// Get transform between pc frame and base_link
if(first_frame_){
// get transform between the pc frame and the output frame
get_transform(kinects_pc_->header.frame_id, "base_link", pc_transform_);
first_frame_ = false;
}
// Transform the pc to base_link
pcl::transformPointCloud(*cluster_cloud_, *cluster_cloud_, pc_transform_.translation, pc_transform_.rotation);
kinects_pc_->header.frame_id = "base_link";
// Clip the pointcloud
pcl::ConditionAnd<PointType>::Ptr height_cond (new pcl::ConditionAnd<PointType> ());
pcl::ConditionalRemoval<PointType> condrem (height_cond);
height_cond->addComparison ( pcl::FieldComparison<PointType>::ConstPtr (new pcl::FieldComparison<PointType> ("z", pcl::ComparisonOps::GT, 0.15)));
height_cond->addComparison ( pcl::FieldComparison<PointType>::ConstPtr (new pcl::FieldComparison<PointType> ("y", pcl::ComparisonOps::GT, -1.0)));
height_cond->addComparison ( pcl::FieldComparison<PointType>::ConstPtr (new pcl::FieldComparison<PointType> ("y", pcl::ComparisonOps::LT, 1.0)));
height_cond->addComparison ( pcl::FieldComparison<PointType>::ConstPtr (new pcl::FieldComparison<PointType> ("x", pcl::ComparisonOps::LT, 1.5)));
height_cond->addComparison ( pcl::FieldComparison<PointType>::ConstPtr (new pcl::FieldComparison<PointType> ("x", pcl::ComparisonOps::GT, -0.5)));
condrem.setInputCloud (cluster_cloud_);
condrem.setKeepOrganized(true);
condrem.filter (*cluster_cloud_);
// Estimate normals
norm_est_.setKSearch(20);
norm_est_.setInputCloud (cluster_cloud_);
norm_est_.compute (*normals_);
// PCL Viewer
if(first_frame_)
viewer_.addPointCloudNormals<PointType, NormalType>(cluster_cloud_, normals_, 1, 0.05, "normals");
else{
viewer_.removePointCloud("normals");
viewer_.addPointCloudNormals<PointType, NormalType>(cluster_cloud_, normals_, 1, 0.05, "normals");
}
viewer_.updatePointCloud(cluster_cloud_,"sample_cloud");
viewer_.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_COLOR, 1.0, 0.0, 0.0, "normals");
viewer_.spinOnce();
bool use_hough_ (true), show_correspondences_(true), show_keypoints_(true);
float model_ss_ (0.005f);
float scene_ss_ (0.005f);
float descr_rad_ (0.036f);
//float rf_rad_ (0.1f);
float rf_rad_(rf_rad);
float cg_size_ (0.2f);
float cg_thresh_ (3.0f);
int knn = 1;
pcl::PointCloud<int> model_keypoints_indices, scene_keypoints_indices;
pcl::PointCloud<PointType>::Ptr model_keypoints, scene_keypoints;
model_keypoints = boost::shared_ptr<pcl::PointCloud<PointType> >(new pcl::PointCloud<PointType> );
scene_keypoints = boost::shared_ptr<pcl::PointCloud<PointType> >(new pcl::PointCloud<PointType> );
pcl::UniformSampling<PointType> uniform_sampling;
uniform_sampling.setInputCloud (robot_pc_);
uniform_sampling.setRadiusSearch (model_ss_);
uniform_sampling.compute(model_keypoints_indices);
std::cout << "Model total points: " << robot_pc_->size () << "; Selected Keypoints: " << model_keypoints_indices.size () << std::endl;
uniform_sampling.setInputCloud (cluster_cloud_);
uniform_sampling.setRadiusSearch (scene_ss_);;
uniform_sampling.compute(scene_keypoints_indices);
std::cout << "Scene total points: " << cluster_cloud_->size () << "; Selected Keypoints: " << scene_keypoints_indices.size () << std::endl;
// Use Keypoint indices to make a PointCloud
model_keypoints->points.resize(model_keypoints_indices.points.size ());
for (size_t i=0; i<model_keypoints_indices.points.size (); ++i)
model_keypoints->points[i].getVector3fMap () = robot_pc_->points[model_keypoints_indices.points[i]].getVector3fMap();
scene_keypoints->points.resize (scene_keypoints_indices.points.size ());
for (size_t i=0; i<scene_keypoints_indices.points.size (); ++i)
scene_keypoints->points[i].getVector3fMap () = cluster_cloud_->points[scene_keypoints_indices.points[i]].getVector3fMap();
std::cout<<"keypoints converted"<<std::endl;
//
// Compute Descriptor for keypoints
//
pcl::PointCloud<DescriptorType>::Ptr model_descriptors (new pcl::PointCloud<DescriptorType> ());
pcl::PointCloud<DescriptorType>::Ptr scene_descriptors (new pcl::PointCloud<DescriptorType> ());
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est;
//descr_est.setKSearch(10);
descr_est.setRadiusSearch (descr_rad_);
descr_est.setInputCloud (model_keypoints);
descr_est.setInputNormals (robot_normals_);
descr_est.setSearchSurface (robot_pc_);
descr_est.compute (*model_descriptors);
std::cout<<"SHOTEstimationOMP done for model"<<std::endl;
pcl::SHOTEstimationOMP<PointType, NormalType, DescriptorType> descr_est2;
// descr_est2.setKSearch(10);
descr_est2.setRadiusSearch (descr_rad_);
descr_est2.setInputCloud (scene_keypoints);
descr_est2.setInputNormals (normals_);
descr_est2.setSearchSurface (cluster_cloud_);
descr_est2.compute (*scene_descriptors);
std::cout<<"SHOTEstimationOMP done for scene"<<std::endl;
//
// Find Model-Scene Correspondences with KdTree
//
pcl::CorrespondencesPtr model_scene_corrs (new pcl::Correspondences ());
pcl::KdTreeFLANN<DescriptorType> match_search;
match_search.setInputCloud (model_descriptors);
std::cout<<"KdTreeFLANN searched"<<std::endl;
// For each scene keypoint descriptor, find nearest neighbor into the model keypoints descriptor cloud and add it to the correspondences vector.
// for (size_t i = 0; i < scene_descriptors->size (); ++i)
// {
// std::vector<int> neigh_indices (1);
// std::vector<float> neigh_sqr_dists (1);
// if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) //skipping NaNs
// {
// continue;
// }
// int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), 1, neigh_indices, neigh_sqr_dists);
// // if(found_neighs == 1 && neigh_sqr_dists[0] < 0.25f) // add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
// if(found_neighs == 1 && neigh_sqr_dists[0] < 0.25f) // add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
// {
// pcl::Correspondence corr (neigh_indices[0], static_cast<int> (i), neigh_sqr_dists[0]);
// model_scene_corrs->push_back (corr);
// }
// }
for (size_t i = 0; i < scene_descriptors->size (); ++i)
{
std::vector<int> neigh_indices (knn);
std::vector<float> neigh_sqr_dists (knn);
if (!pcl_isfinite (scene_descriptors->at (i).descriptor[0])) //skipping NaNs
{
continue;
}
int found_neighs = match_search.nearestKSearch (scene_descriptors->at (i), knn, neigh_indices, neigh_sqr_dists);
for(int k = 0; k < found_neighs; k++)
{
if(found_neighs == 1 && neigh_sqr_dists[k] < 0.1f) // add match only if the squared descriptor distance is less than 0.25 (SHOT descriptor distances are between 0 and 1 by design)
{
pcl::Correspondence corr (neigh_indices[k], static_cast<int> (i), neigh_sqr_dists[k]);
model_scene_corrs->push_back (corr);
}
}
}
std::cout << "Correspondences found: " << model_scene_corrs->size () << std::endl;
//
// Actual Clustering
//
std::vector<Eigen::Matrix4f, Eigen::aligned_allocator<Eigen::Matrix4f> > rototranslations;
std::vector<pcl::Correspondences> clustered_corrs;
std::cout<<"Actual clustering done"<<std::endl;
// Using Hough3D
if (use_hough_)
{
//
// Compute (Keypoints) Reference Frames only for Hough
//
pcl::PointCloud<RFType>::Ptr model_rf (new pcl::PointCloud<RFType> ());
pcl::PointCloud<RFType>::Ptr scene_rf (new pcl::PointCloud<RFType> ());
pcl::BOARDLocalReferenceFrameEstimation<PointType, NormalType, RFType> rf_est;
rf_est.setFindHoles (true);
rf_est.setRadiusSearch (rf_rad_);
rf_est.setInputCloud (model_keypoints);
rf_est.setInputNormals (robot_normals_);
rf_est.setSearchSurface (robot_pc_);
rf_est.compute (*model_rf);
rf_est.setInputCloud (scene_keypoints);
rf_est.setInputNormals (normals_);
rf_est.setSearchSurface (cluster_cloud_);
rf_est.compute (*scene_rf);
// Clustering
pcl::Hough3DGrouping<PointType, PointType, RFType, RFType> clusterer;
clusterer.setHoughBinSize (cg_size_);
clusterer.setHoughThreshold (cg_thresh_);
clusterer.setUseInterpolation (true);
clusterer.setUseDistanceWeight (false);
clusterer.setInputCloud (model_keypoints);
clusterer.setInputRf (model_rf);
clusterer.setSceneCloud (scene_keypoints);
clusterer.setSceneRf (scene_rf);
clusterer.setModelSceneCorrespondences (model_scene_corrs);
//clusterer.cluster (clustered_corrs);
clusterer.recognize (rototranslations, clustered_corrs);
}
else // Using GeometricConsistency
{
pcl::GeometricConsistencyGrouping<PointType, PointType> gc_clusterer;
gc_clusterer.setGCSize (cg_size_);
gc_clusterer.setGCThreshold (cg_thresh_);
gc_clusterer.setInputCloud (model_keypoints);
gc_clusterer.setSceneCloud (scene_keypoints);
gc_clusterer.setModelSceneCorrespondences (model_scene_corrs);
//gc_clusterer.cluster (clustered_corrs);
gc_clusterer.recognize (rototranslations, clustered_corrs);
}
//
// Output results
//
std::cout << "Model instances found: " << rototranslations.size () << std::endl;
for (size_t i = 0; i < rototranslations.size (); ++i)
{
std::cout << "\n Instance " << i + 1 << ":" << std::endl;
std::cout << " Correspondences belonging to this instance: " << clustered_corrs[i].size () << std::endl;
// Print the rotation matrix and translation vector
Eigen::Matrix3f rotation = rototranslations[i].block<3,3>(0, 0);
Eigen::Vector3f translation = rototranslations[i].block<3,1>(0, 3);
printf ("\n");
printf (" | %6.3f %6.3f %6.3f | \n", rotation (0,0), rotation (0,1), rotation (0,2));
printf (" R = | %6.3f %6.3f %6.3f | \n", rotation (1,0), rotation (1,1), rotation (1,2));
printf (" | %6.3f %6.3f %6.3f | \n", rotation (2,0), rotation (2,1), rotation (2,2));
printf ("\n");
printf (" t = < %0.3f, %0.3f, %0.3f >\n", translation (0), translation (1), translation (2));
}
// Visualization
// White cloud: scene
// Yellow cloud: off_scene_MODEL
// Blue cloud: scene keypoints and off_scene_MODEL keypoints
// Red cloud: rotated model
// Green lines: correspondences between
pcl::visualization::PCLVisualizer viewer ("Correspondence Grouping");
viewer.addPointCloud (cluster_cloud_, "scene_cloud");
pcl::PointCloud<PointType>::Ptr off_scene_model (new pcl::PointCloud<PointType> ());
pcl::PointCloud<PointType>::Ptr off_scene_model_keypoints (new pcl::PointCloud<PointType> ());
if (show_correspondences_ || show_keypoints_)
{
// We are translating the model so that it doesn't end in the middle of the scene representation
pcl::transformPointCloud (*robot_pc_, *off_scene_model, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::transformPointCloud (*model_keypoints, *off_scene_model_keypoints, Eigen::Vector3f (-1,0,0), Eigen::Quaternionf (1, 0, 0, 0));
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_color_handler (off_scene_model, 255, 255, 128);
viewer.addPointCloud (off_scene_model, off_scene_model_color_handler, "off_scene_model");
}
if (show_keypoints_)
{
pcl::visualization::PointCloudColorHandlerCustom<PointType> scene_keypoints_color_handler (scene_keypoints, 0, 0, 255);
viewer.addPointCloud (scene_keypoints, scene_keypoints_color_handler, "scene_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "scene_keypoints");
pcl::visualization::PointCloudColorHandlerCustom<PointType> off_scene_model_keypoints_color_handler (off_scene_model_keypoints, 0, 0, 255);
viewer.addPointCloud (off_scene_model_keypoints, off_scene_model_keypoints_color_handler, "off_scene_model_keypoints");
viewer.setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 5, "off_scene_model_keypoints");
}
for (size_t i = 0; i < rototranslations.size (); ++i)
{
pcl::PointCloud<PointType>::Ptr rotated_model (new pcl::PointCloud<PointType> ());
pcl::transformPointCloud (*robot_pc_, *rotated_model, rototranslations[i]);
std::stringstream ss_cloud;
ss_cloud << "instance" << i;
pcl::visualization::PointCloudColorHandlerCustom<PointType> rotated_model_color_handler (rotated_model, 255, 0, 0);
viewer.addPointCloud (rotated_model, rotated_model_color_handler, ss_cloud.str ());
if (show_correspondences_)
{
for (size_t j = 0; j < clustered_corrs[i].size (); ++j)
{
std::stringstream ss_line;
ss_line << "correspondence_line" << i << "_" << j;
PointType& model_point = off_scene_model_keypoints->at (clustered_corrs[i][j].index_query);
PointType& scene_point = scene_keypoints->at (clustered_corrs[i][j].index_match);
// We are drawing a line for each pair of clustered correspondences found between the model and the scene
viewer.addLine<PointType, PointType> (model_point, scene_point, 0, 255, 0, ss_line.str ());
}
}
}
while (!viewer.wasStopped ())
{
viewer.spinOnce ();
}
exit(0);
}
