Esempio n. 1
0
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;

}
Esempio n. 2
0
// 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);
}