int sac_ia_alignment(pcl::PointCloud<pcl::PointXYZ>::Ptr prev_cloud,
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_in) {
pcl::PointCloud<pcl::PointXYZ>::Ptr temp_cloud(new pcl::PointCloud<pcl::PointXYZ>(*prev_cloud));
std::cout << "Performing Sample Consensus Initial Alignment.. "
<< std::endl;
FeatureCloud targetCloud;
FeatureCloud templateCloud;
targetCloud.setInputCloud(temp_cloud);
templateCloud.setInputCloud(cloud_in);
TemplateAlignment templateAlign;
templateAlign.addTemplateCloud(templateCloud);
templateAlign.setTargetCloud(targetCloud);
Result bestAlignment;
std::cout << "entering alignment" << std::endl;
templateAlign.findBestAlignment(bestAlignment);
std::cout << "exiting alignment" << std::endl;
printf("Fitness Score: %f \n", bestAlignment.fitness_score);
Eigen::Matrix3f rotation = bestAlignment.final_transformation.block<3,3>(0, 0);
Eigen::Vector3f translation =
bestAlignment.final_transformation.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));
pcl::transformPointCloud(*templateCloud.getPointCloud(), *cloud_in,
bestAlignment.final_transformation);
std::cout << "***Initial alignment complete***" << std::endl;
}
// Align a collection of object templates to a sample point cloud
void cloud_cb( const sensor_msgs::PointCloud2ConstPtr& input )
{
if( controllerState != 1 )
return;
//--- Convert Incoming Cloud --- //
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud( new pcl::PointCloud<pcl::PointXYZ> );
pcl::fromROSMsg( *input, *cloud );
// --- Z-Filter And Downsample Cloud --- //
// Preprocess the cloud by removing distant points
pcl::PassThrough<pcl::PointXYZ> pass_z;
pass_z.setInputCloud( cloud );
pass_z.setFilterFieldName( "z" );
pass_z.setFilterLimits( 0, 1.75 );
pass_z.filter( *cloud );
pcl::PassThrough<pcl::PointXYZ> pass_y;
pass_y.setInputCloud( cloud );
pass_y.setFilterFieldName("y");
pass_y.setFilterLimits( -0.5, 0.2 );
pass_y.filter( *cloud );
pcl::PassThrough<pcl::PointXYZ> pass_x;
pass_x.setInputCloud( cloud );
pass_x.setFilterFieldName("x");
pass_x.setFilterLimits( -0.5, 0.5 );
pass_x.filter( *cloud );
// It is possible to not have any points after z-filtering (ex. if we are looking up).
// Just bail if there is nothing left.
if( cloud->points.size() == 0 )
return;
//visualize( cloud, visualizer_o_Ptr );
// --- Calculate Scene Normals --- //
pcl::PointCloud<pcl::Normal>::Ptr pSceneNormals( new pcl::PointCloud<pcl::Normal>() );
pcl::NormalEstimationOMP<pcl::PointXYZ, pcl::Normal> normEst;
normEst.setKSearch(10);
normEst.setInputCloud( cloud );
normEst.compute( *pSceneNormals );
// --- Get Rid Of Floor --- //
pcl::PointIndices::Ptr inliers_plane( new pcl::PointIndices );
pcl::ModelCoefficients::Ptr coefficients_plane( new pcl::ModelCoefficients );
pcl::SACSegmentationFromNormals<pcl::PointXYZ, pcl::Normal> seg1;
seg1.setOptimizeCoefficients( true );
seg1.setModelType( pcl::SACMODEL_NORMAL_PLANE );
seg1.setNormalDistanceWeight( 0.05 );
seg1.setMethodType( pcl::SAC_RANSAC );
seg1.setMaxIterations( 100 );
seg1.setDistanceThreshold( 0.075 );
seg1.setInputCloud( cloud );
seg1.setInputNormals( pSceneNormals );
// Obtain the plane inliers and coefficients
seg1.segment( *inliers_plane, *coefficients_plane );
// Extract the planar inliers from the input cloud
pcl::ExtractIndices<pcl::PointXYZ> extract;
extract.setInputCloud( cloud );
extract.setIndices( inliers_plane );
extract.setNegative( false );
// Write the planar inliers to disk
pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_plane( new pcl::PointCloud<pcl::PointXYZ> );
extract.filter( *cloud_plane );
// Remove the planar inliers, extract the rest
pcl::PointCloud<pcl::PointXYZ>::Ptr filteredScene( new pcl::PointCloud<pcl::PointXYZ> );
extract.setNegative( true );
extract.filter( *filteredScene );
pcl::ExtractIndices<pcl::Normal> extract_normals;
pcl::PointCloud<pcl::Normal>::Ptr filteredSceneNormals( new pcl::PointCloud<pcl::Normal> );
extract_normals.setNegative( true );
extract_normals.setInputCloud( pSceneNormals );
extract_normals.setIndices( inliers_plane );
extract_normals.filter( *filteredSceneNormals );
if( filteredScene->points.size() == 0 )
return;
// --- Set Our Target Cloud --- //
// Assign to the target FeatureCloud
FeatureCloud target_cloud;
target_cloud.setInputCloud( filteredScene );
// --- Visualize the Filtered Cloud --- //
//visualize( filteredScene, visualizer_o_Ptr );
// --- Set Input Cloud For Alignment --- //
template_align.setTargetCloud( target_cloud );
// --- Align Templates --- //
std::cout << "Searching for best fit" << std::endl;
// Find the best template alignment
TemplateAlignment::Result best_alignment;
int best_index = template_align.findBestAlignment( best_alignment );
std::cerr << "Best alignment index: " << best_index << std::endl;
const FeatureCloud &best_template = object_templates[best_index];
// --- Report Best Match --- //
// Print the alignment fitness score (values less than 0.00002 are good)
std::cerr << "Best fitness score: " << best_alignment.fitness_score << std::endl;
// Print the rotation matrix and translation vector
Eigen::Matrix3f rotation = best_alignment.final_transformation.block<3,3>(0, 0);
Eigen::Vector3f translation = best_alignment.final_transformation.block<3,1>(0, 3);
std::cerr << std::setprecision(3);
std::cerr << std::endl;
std::cerr << " | " << rotation(0,0) << " " << rotation(0,1) << " " << rotation(0,2) << " | " << std::endl;
std::cerr << "R = | " << rotation(1,0) << " " << rotation(1,1) << " " << rotation(1,2) << " | " << std::endl;
std::cerr << " | " << rotation(2,0) << " " << rotation(2,1) << " " << rotation(2,2) << " | " << std::endl;
std::cerr << std::endl;
std::cerr << "t = < " << translation(0) << ", " << translation(1) << ", " << translation(2) << " >" << std::endl << std::endl;
// pcl::PointCloud<pcl::PointXYZ> transformedCloud;
// pcl::transformPointCloud( *best_template.getPointCloud(), transformedCloud, best_alignment.final_transformation);
// visualize( filteredScene, transformedCloud.makeShared(), visualizer_o_Ptr );
// --- Publish --- //
// TODO: Clean up this part.
geometry_msgs::Pose pose;
tf::Matrix3x3 rot( rotation(0,0), rotation(0,1), rotation(0,2),
rotation(1,0), rotation(1,1), rotation(1,2),
rotation(2,0), rotation(2,1), rotation(2,2) );
tf::Quaternion q;
rot.getRotation(q);
pose.orientation.w = q.getW();
pose.orientation.x = q.getX();
pose.orientation.y = q.getY();
pose.orientation.z = q.getZ();
tf::Vector3 t( translation(0), translation(1), translation(2) );
pose.position.x = t.getX();
pose.position.y = t.getY();
pose.position.z = t.getZ();
std::cerr << "Publishing" << std::endl;
pub.publish(pose);
sensor_msgs::PointCloud2 toPub;
pcl::toROSMsg( *filteredScene, toPub );
cloud_pub.publish(toPub);
}