TEST (PCL, VFHEstimation)
{
  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  // estimate
  n.compute (*normals);
  VFHEstimation<PointXYZ, Normal, VFHSignature308> vfh;
  vfh.setInputNormals (normals);

  //  PointCloud<PointNormal> cloud_normals;
  //  concatenateFields (cloud, normals, cloud_normals);
  //  savePCDFile ("bun0_n.pcd", cloud_normals);

  // Object
  PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308> ());

  // set parameters
  vfh.setInputCloud (cloud.makeShared ());
  vfh.setIndices (indicesptr);
  vfh.setSearchMethod (tree);

  // estimate
  vfh.compute (*vfhs);
  EXPECT_EQ (int (vfhs->points.size ()), 1);

  //for (size_t d = 0; d < 308; ++d)
  //  std::cerr << vfhs.points[0].histogram[d] << std::endl;
}
Exemple #2
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void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, sensor_msgs::PointCloud2 &output,
         int k, double radius)
{
  // Convert data to PointCloud<T>
  PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
  fromROSMsg (*input, *xyz);

  // Estimate
  TicToc tt;
  tt.tic ();
  
  print_highlight (stderr, "Computing ");

  NormalEstimation<pcl::PointXYZ, pcl::Normal> ne;
  ne.setInputCloud (xyz);
//  ne.setSearchMethod (pcl::search::KdTree<pcl::PointXYZ>::Ptr (new pcl::search::KdTree<pcl::PointXYZ>));
  ne.setKSearch (k);
  ne.setRadiusSearch (radius);
  
  PointCloud<Normal> normals;
  ne.compute (normals);

  print_info ("[done, "); print_value ("%g", tt.toc ()); print_info (" ms : "); print_value ("%d", normals.width * normals.height); print_info (" points]\n");

  // Convert data back
  sensor_msgs::PointCloud2 output_normals;
  toROSMsg (normals, output_normals);
  concatenateFields (*input, output_normals, output);
}
Exemple #3
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TEST (PCL, CVFHEstimation)
{
  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  // estimate
  n.compute (*normals);
  CVFHEstimation<PointXYZ, Normal, VFHSignature308> cvfh;
  cvfh.setInputNormals (normals);

  // Object
  PointCloud<VFHSignature308>::Ptr vfhs (new PointCloud<VFHSignature308> ());

  // set parameters
  cvfh.setInputCloud (cloud.makeShared ());
  cvfh.setIndices (indicesptr);
  cvfh.setSearchMethod (tree);

  // estimate
  cvfh.compute (*vfhs);
  EXPECT_EQ (static_cast<int>(vfhs->points.size ()), 1);
}
void SurfaceTriangulation::perform(int ksearch)
{
    PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
    PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
    search::KdTree<PointXYZ>::Ptr tree;
    search::KdTree<PointNormal>::Ptr tree2;

    cloud->reserve(myPoints.size());
    for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
        if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
            cloud->push_back(PointXYZ(it->x, it->y, it->z));
    }

    // Create search tree
    tree.reset (new search::KdTree<PointXYZ> (false));
    tree->setInputCloud (cloud);

    // Normal estimation
    NormalEstimation<PointXYZ, Normal> n;
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    n.setInputCloud (cloud);
    //n.setIndices (indices[B);
    n.setSearchMethod (tree);
    n.setKSearch (ksearch);
    n.compute (*normals);

    // Concatenate XYZ and normal information
    pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);

    // Create search tree
    tree2.reset (new search::KdTree<PointNormal>);
    tree2->setInputCloud (cloud_with_normals);

    // Init objects
    GreedyProjectionTriangulation<PointNormal> gp3;

    // Set parameters
    gp3.setInputCloud (cloud_with_normals);
    gp3.setSearchMethod (tree2);
    gp3.setSearchRadius (searchRadius);
    gp3.setMu (mu);
    gp3.setMaximumNearestNeighbors (100);
    gp3.setMaximumSurfaceAngle(M_PI/4); // 45 degrees
    gp3.setMinimumAngle(M_PI/18); // 10 degrees
    gp3.setMaximumAngle(2*M_PI/3); // 120 degrees
    gp3.setNormalConsistency(false);
    gp3.setConsistentVertexOrdering(true);

    // Reconstruct
    PolygonMesh mesh;
    gp3.reconstruct (mesh);

    MeshConversion::convert(mesh, myMesh);

    // Additional vertex information
    //std::vector<int> parts = gp3.getPartIDs();
    //std::vector<int> states = gp3.getPointStates();
}
Exemple #5
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void
computeFeatureViaNormals (const pcl::PCLPointCloud2::ConstPtr &input, pcl::PCLPointCloud2 &output,
                          int argc, char** argv, bool set_search_flag = true)
{
    int n_k = default_n_k;
    int f_k = default_f_k;
    double n_radius = default_n_radius;
    double f_radius = default_f_radius;
    parse_argument (argc, argv, "-n_k", n_k);
    parse_argument (argc, argv, "-n_radius", n_radius);
    parse_argument (argc, argv, "-f_k", f_k);
    parse_argument (argc, argv, "-f_radius", f_radius);

    // Convert data to PointCloud<PointIn>
    typename PointCloud<PointIn>::Ptr xyz (new PointCloud<PointIn>);
    fromPCLPointCloud2 (*input, *xyz);

    // Estimate
    TicToc tt;
    tt.tic ();

    print_highlight (stderr, "Computing ");

    NormalEstimation<PointIn, NormalT> ne;
    ne.setInputCloud (xyz);
    ne.setSearchMethod (typename pcl::search::KdTree<PointIn>::Ptr (new pcl::search::KdTree<PointIn>));
    ne.setKSearch (n_k);
    ne.setRadiusSearch (n_radius);

    typename PointCloud<NormalT>::Ptr normals = typename PointCloud<NormalT>::Ptr (new PointCloud<NormalT>);
    ne.compute (*normals);

    FeatureAlgorithm feature_est;
    feature_est.setInputCloud (xyz);
    feature_est.setInputNormals (normals);

    feature_est.setSearchMethod (typename pcl::search::KdTree<PointIn>::Ptr (new pcl::search::KdTree<PointIn>));

    PointCloud<PointOut> output_features;

    if (set_search_flag) {
        feature_est.setKSearch (f_k);
        feature_est.setRadiusSearch (f_radius);
    }

    feature_est.compute (output_features);

    print_info ("[done, ");
    print_value ("%g", tt.toc ());
    print_info (" ms : ");
    print_value ("%d", output.width * output.height);
    print_info (" points]\n");

    // Convert data back
    toPCLPointCloud2 (output_features, output);
}
void FeatSegment::segment(const PointCloud<PointXYZRGB >::Ptr cloud,  PointCloud<PointXYZRGB >::Ptr outcloud)
{
	// PARAM - Lots of them
	int min_pts_per_cluster = 10;
	int max_pts_per_cluster = INT_MAX; //3000000;
	assert(max_pts_per_cluster > 3000000); // Avoid overflow
    int number_neighbours = 50; // PARAM
    float radius = 0.025; // 0.025 PARAM
    float angle = 0.52; // PARAM

	// Required KdTrees
    pcl::search::KdTree<PointXYZRGB >::Ptr NormalsTree(new pcl::search::KdTree<PointXYZRGB >);
    pcl::search::KdTree<PointXYZRGB >::Ptr ClustersTree(new pcl::search::KdTree<PointXYZRGB >);
    PointCloud<Normal >::Ptr CloudNormals(new PointCloud<Normal >);
    NormalEstimation<PointXYZRGB, Normal > NormalsFinder;
    std::vector<PointIndices > clusters;

	ClustersTree->setInputCloud(cloud);

	// Set normal estimation parameters
    NormalsFinder.setKSearch(number_neighbours);
    NormalsFinder.setSearchMethod(NormalsTree);
    NormalsFinder.setInputCloud(cloud);
    NormalsFinder.compute(*CloudNormals);

    extractEuclideanClusters(cloud, CloudNormals, ClustersTree, radius, clusters, angle, min_pts_per_cluster, max_pts_per_cluster);
    fprintf(stderr, "Number of clusters found matching the given constraints: %d.", (int)clusters.size ());

	std::ofstream FileStr2;
	FileStr2.open("live_segments.txt", ios::app); // NOTE: Don't add the ios:trunc flag here!
	// Copy to clusters to segments
	for (size_t i = 0; i < clusters.size (); ++i)
	{
		if(FileStr2.is_open())
		{
			for(int j = 0; j < clusters[i].indices.size(); ++j)
			{
				if(j == clusters[i].indices.size() - 1)
				{
					FileStr2 << clusters[i].indices.at(j) << endl;
					continue; // Also break;
				}
				FileStr2 << clusters[i].indices.at(j) << " ";
			}

			FeatPointCloud * Segment = new FeatPointCloud(m_SceneCloud, clusters[i].indices, m_ConfigFName);
			m_Segments.push_back(Segment);
		}
		else
			cout << "Failed to open file.\n";
	}
	FileStr2.close();
}
void PoissonReconstruction::perform(int ksearch)
{
    PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
    PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
    search::KdTree<PointXYZ>::Ptr tree;
    search::KdTree<PointNormal>::Ptr tree2;

    cloud->reserve(myPoints.size());
    for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
        if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
            cloud->push_back(PointXYZ(it->x, it->y, it->z));
    }

    // Create search tree
    tree.reset (new search::KdTree<PointXYZ> (false));
    tree->setInputCloud (cloud);

    // Normal estimation
    NormalEstimation<PointXYZ, Normal> n;
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    n.setInputCloud (cloud);
    //n.setIndices (indices[B);
    n.setSearchMethod (tree);
    n.setKSearch (ksearch);
    n.compute (*normals);

    // Concatenate XYZ and normal information
    pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);

    // Create search tree
    tree2.reset (new search::KdTree<PointNormal>);
    tree2->setInputCloud (cloud_with_normals);

    // Init objects
    Poisson<PointNormal> poisson;

    // Set parameters
    poisson.setInputCloud (cloud_with_normals);
    poisson.setSearchMethod (tree2);
    if (depth >= 1)
        poisson.setDepth(depth);
    if (solverDivide >= 1)
        poisson.setSolverDivide(solverDivide);
    if (samplesPerNode >= 1.0f)
        poisson.setSamplesPerNode(samplesPerNode);

    // Reconstruct
    PolygonMesh mesh;
    poisson.reconstruct (mesh);

    MeshConversion::convert(mesh, myMesh);
}
Exemple #8
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 /** estimate the normals of a point cloud */
 static PointCloud<Normal>::Ptr
 compute_pcn(PointCloud<PointXYZ>::ConstPtr in, float vx, float vy, float vz)
 {
   PointCloud<Normal>::Ptr pcn (new PointCloud<Normal>());
   NormalEstimation<PointXYZ, Normal> ne;
   search::KdTree<PointXYZ>::Ptr kdt (new search::KdTree<PointXYZ>());
   ne.setInputCloud(in);
   ne.setSearchMethod(kdt);
   ne.setKSearch(20);
   ne.setViewPoint(vx, vy, vz);
   ne.compute(*pcn);
   return pcn;
 }
void Reen::MarchingCubesRBF::perform(int ksearch)
{
    PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
    PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
    search::KdTree<PointXYZ>::Ptr tree;
    search::KdTree<PointNormal>::Ptr tree2;

    cloud->reserve(myPoints.size());
    for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
        if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
            cloud->push_back(PointXYZ(it->x, it->y, it->z));
    }

    // Create search tree
    tree.reset (new search::KdTree<PointXYZ> (false));
    tree->setInputCloud (cloud);

    // Normal estimation
    NormalEstimation<PointXYZ, Normal> n;
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    n.setInputCloud (cloud);
    //n.setIndices (indices[B);
    n.setSearchMethod (tree);
    n.setKSearch (ksearch);
    n.compute (*normals);

    // Concatenate XYZ and normal information
    pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);

    // Create search tree
    tree2.reset (new search::KdTree<PointNormal>);
    tree2->setInputCloud (cloud_with_normals);

    // Init objects
    pcl::MarchingCubesRBF<PointNormal> rbf;

    // Set parameters
    rbf.setIsoLevel (0);
    rbf.setGridResolution (60, 60, 60);
    rbf.setPercentageExtendGrid (0.1f);
    rbf.setOffSurfaceDisplacement (0.02f);

    rbf.setInputCloud (cloud_with_normals);
    rbf.setSearchMethod (tree2);

    // Reconstruct
    PolygonMesh mesh;
    rbf.reconstruct (mesh);

    MeshConversion::convert(mesh, myMesh);
}
void GridReconstruction::perform(int ksearch)
{
    PointCloud<PointXYZ>::Ptr cloud (new PointCloud<PointXYZ>);
    PointCloud<PointNormal>::Ptr cloud_with_normals (new PointCloud<PointNormal>);
    search::KdTree<PointXYZ>::Ptr tree;
    search::KdTree<PointNormal>::Ptr tree2;

    cloud->reserve(myPoints.size());
    for (Points::PointKernel::const_iterator it = myPoints.begin(); it != myPoints.end(); ++it) {
        if (!boost::math::isnan(it->x) && !boost::math::isnan(it->y) && !boost::math::isnan(it->z))
            cloud->push_back(PointXYZ(it->x, it->y, it->z));
    }

    // Create search tree
    tree.reset (new search::KdTree<PointXYZ> (false));
    tree->setInputCloud (cloud);

    // Normal estimation
    NormalEstimation<PointXYZ, Normal> n;
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    n.setInputCloud (cloud);
    //n.setIndices (indices[B);
    n.setSearchMethod (tree);
    n.setKSearch (ksearch);
    n.compute (*normals);

    // Concatenate XYZ and normal information
    pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);

    // Create search tree
    tree2.reset (new search::KdTree<PointNormal>);
    tree2->setInputCloud (cloud_with_normals);

    // Init objects
    GridProjection<PointNormal> grid;

    // Set parameters
    grid.setResolution(0.005); 
    grid.setPaddingSize(3); 
    grid.setNearestNeighborNum(100); 
    grid.setMaxBinarySearchLevel(10);
    grid.setInputCloud (cloud_with_normals);
    grid.setSearchMethod (tree2);

    // Reconstruct
    PolygonMesh mesh;
    grid.reconstruct (mesh);

    MeshConversion::convert(mesh, myMesh);
}
Exemple #11
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void getCylClusters (boost::shared_ptr<PointCloud<T> > sceneCloud, vector<boost::shared_ptr<PointCloud<T> > > &outVector) {

  typedef typename pcl::search::KdTree<T>::Ptr my_KdTreePtr;
  typedef typename pcl::PointCloud<T>::Ptr my_PointCloudPtr;

  NormalEstimation<T, Normal> ne;
  SACSegmentationFromNormals<T, Normal> seg; 
  my_KdTreePtr tree (new pcl::search::KdTree<T> ());
  PointCloud<Normal>::Ptr cloud_normals (new PointCloud<pcl::Normal>);

  ModelCoefficients::Ptr coefficients_cylinder (new pcl::ModelCoefficients);
  PointIndices::Ptr  inliers_cylinder (new pcl::PointIndices);
  ExtractIndices<T> extract;

  // Estimate point normals
  ne.setSearchMethod (tree);
  ne.setInputCloud (sceneCloud);
  ne.setKSearch (50);
  ne.compute (*cloud_normals);

  seg.setOptimizeCoefficients (true);
  seg.setModelType (SACMODEL_CYLINDER);
  seg.setMethodType (SAC_RANSAC);
  seg.setNormalDistanceWeight (0.1);
  seg.setMaxIterations (10000);
  seg.setDistanceThreshold (0.05);
  seg.setRadiusLimits (0, 0.1);
  seg.setInputCloud (sceneCloud);
  seg.setInputNormals (cloud_normals);

  // Obtain the cylinder inliers and coefficients
  seg.segment (*inliers_cylinder, *coefficients_cylinder);
  cout << " Number of inliers vs total number of points " << inliers_cylinder->indices.size() << " vs " << sceneCloud->size() << endl; 
  // Write the cylinder inliers to disk
  extract.setInputCloud (sceneCloud);
  extract.setIndices (inliers_cylinder);
  extract.setNegative (false);
  my_PointCloudPtr cloud_cylinder (new PointCloud<T> ());
  extract.filter (*cloud_cylinder);
  
  outVector.push_back(cloud_cylinder);
}
Exemple #12
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void
compute (const sensor_msgs::PointCloud2::ConstPtr &input, sensor_msgs::PointCloud2 &output,
         int k, double radius)
{
  // Convert data to PointCloud<T>
  PointCloud<PointXYZ>::Ptr xyz (new PointCloud<PointXYZ>);
  fromROSMsg (*input, *xyz);

  TicToc tt;
  tt.tic ();
 
  PointCloud<Normal> normals;

  // Try our luck with organized integral image based normal estimation
  if (xyz->isOrganized ())
  {
    IntegralImageNormalEstimation<PointXYZ, Normal> ne;
    ne.setInputCloud (xyz);
    ne.setNormalEstimationMethod (IntegralImageNormalEstimation<PointXYZ, Normal>::COVARIANCE_MATRIX);
    ne.setNormalSmoothingSize (float (radius));
    ne.setDepthDependentSmoothing (true);
    ne.compute (normals);
  }
  else
  {
    NormalEstimation<PointXYZ, Normal> ne;
    ne.setInputCloud (xyz);
    ne.setSearchMethod (search::KdTree<PointXYZ>::Ptr (new search::KdTree<PointXYZ>));
    ne.setKSearch (k);
    ne.setRadiusSearch (radius);
    ne.compute (normals);
  }

  print_highlight ("Computed normals in "); print_value ("%g", tt.toc ()); print_info (" ms for "); print_value ("%d", normals.width * normals.height); print_info (" points.\n");

  // Convert data back
  sensor_msgs::PointCloud2 output_normals;
  toROSMsg (normals, output_normals);
  concatenateFields (*input, output_normals, output);
}
Exemple #13
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TEST (PCL, NormalEstimation)
{
  tree.reset (new search::KdTree<PointXYZ> (false));
  n.setSearchMethod (tree);
  n.setKSearch (10);

  n.setInputCloud (cloud.makeShared ());

  PointCloud<Normal> output;
  n.compute (output);

  EXPECT_EQ (output.points.size (), cloud.points.size ());
  EXPECT_EQ (output.width, cloud.width);
  EXPECT_EQ (output.height, cloud.height);

  for (size_t i = 0; i < cloud.points.size (); ++i)
  {
    EXPECT_NEAR (fabs (output.points[i].normal_x),   0, 1e-2);
    EXPECT_NEAR (fabs (output.points[i].normal_y),   0, 1e-2);
    EXPECT_NEAR (fabs (output.points[i].normal_z), 1.0, 1e-2);
  }
}
Exemple #14
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/* ---[ */
int
main (int argc, char** argv)
{
  // Load two standard PCD files from disk
  if (argc < 3)
  {
    std::cerr << "No test files given. Please download `sac_plane_test.pcd` and 'cturtle.pcd' and pass them path to the test." << std::endl;
    return (-1);
  }

  // Load in the point clouds
  io::loadPCDFile (argv[1], *cloud_walls);
  io::loadPCDFile (argv[2], *cloud_turtle);



  // Compute the normals for each cloud, and then clean them up of any NaN values
  NormalEstimation<PointXYZ,PointNormal> ne;
  ne.setInputCloud (cloud_walls);
  ne.setRadiusSearch (0.05);
  ne.compute (*cloud_walls_normals);
  copyPointCloud (*cloud_walls, *cloud_walls_normals);

  std::vector<int> aux_indices;
  removeNaNFromPointCloud (*cloud_walls_normals, *cloud_walls_normals, aux_indices);
  removeNaNNormalsFromPointCloud (*cloud_walls_normals, *cloud_walls_normals, aux_indices);

  ne = NormalEstimation<PointXYZ, PointNormal> ();
  ne.setInputCloud (cloud_turtle);
  ne.setKSearch (5);
  ne.compute (*cloud_turtle_normals);
  copyPointCloud (*cloud_turtle, *cloud_turtle_normals);
  removeNaNFromPointCloud (*cloud_turtle_normals, *cloud_turtle_normals, aux_indices);
  removeNaNNormalsFromPointCloud (*cloud_turtle_normals, *cloud_turtle_normals, aux_indices);

  testing::InitGoogleTest (&argc, argv);
  return (RUN_ALL_TESTS ());
}
TEST (PCL, BoundaryEstimation)
{
  Eigen::Vector4f u = Eigen::Vector4f::Zero ();
  Eigen::Vector4f v = Eigen::Vector4f::Zero ();

  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (static_cast<int> (indices.size ()));
  // estimate
  n.compute (*normals);

  BoundaryEstimation<PointXYZ, Normal, Boundary> b;
  b.setInputNormals (normals);
  EXPECT_EQ (b.getInputNormals (), normals);

  // getCoordinateSystemOnPlane
  for (size_t i = 0; i < normals->points.size (); ++i)
  {
    b.getCoordinateSystemOnPlane (normals->points[i], u, v);
    Vector4fMap n4uv = normals->points[i].getNormalVector4fMap ();
    EXPECT_NEAR (n4uv.dot(u), 0, 1e-4);
    EXPECT_NEAR (n4uv.dot(v), 0, 1e-4);
    EXPECT_NEAR (u.dot(v), 0, 1e-4);
  }

  // isBoundaryPoint (indices)
  bool pt = false;
  pt = b.isBoundaryPoint (cloud, 0, indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) / 3, indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) / 2, indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, static_cast<int> (indices.size ()) - 1, indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, true);

  // isBoundaryPoint (points)
  pt = false;
  pt = b.isBoundaryPoint (cloud, cloud.points[0], indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () / 3], indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () / 2], indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, false);
  pt = b.isBoundaryPoint (cloud, cloud.points[indices.size () - 1], indices, u, v, float (M_PI) / 2.0);
  EXPECT_EQ (pt, true);

  // Object
  PointCloud<Boundary>::Ptr bps (new PointCloud<Boundary> ());

  // set parameters
  b.setInputCloud (cloud.makeShared ());
  b.setIndices (indicesptr);
  b.setSearchMethod (tree);
  b.setKSearch (static_cast<int> (indices.size ()));

  // estimate
  b.compute (*bps);
  EXPECT_EQ (bps->points.size (), indices.size ());

  pt = bps->points[0].boundary_point;
  EXPECT_EQ (pt, false);
  pt = bps->points[indices.size () / 3].boundary_point;
  EXPECT_EQ (pt, false);
  pt = bps->points[indices.size () / 2].boundary_point;
  EXPECT_EQ (pt, false);
  pt = bps->points[indices.size () - 1].boundary_point;
  EXPECT_EQ (pt, true);
}
TEST (PCL, PrincipalCurvaturesEstimation)
{
    float pcx, pcy, pcz, pc1, pc2;

    // Estimate normals first
    NormalEstimation<PointXYZ, Normal> n;
    PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
    // set parameters
    n.setInputCloud (cloud.makeShared ());
    boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
    n.setIndices (indicesptr);
    n.setSearchMethod (tree);
    n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
    // estimate
    n.compute (*normals);

    PrincipalCurvaturesEstimation<PointXYZ, Normal, PrincipalCurvatures> pc;
    pc.setInputNormals (normals);
    EXPECT_EQ (pc.getInputNormals (), normals);

    // computePointPrincipalCurvatures (indices)
    pc.computePointPrincipalCurvatures (*normals, 0, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (fabs (pcx), 0.98509, 1e-4);
    EXPECT_NEAR (fabs (pcy), 0.10714, 1e-4);
    EXPECT_NEAR (fabs (pcz), 0.13462, 1e-4);
    EXPECT_NEAR (pc1, 0.23997423052787781, 1e-4);
    EXPECT_NEAR (pc2, 0.19400238990783691, 1e-4);

    pc.computePointPrincipalCurvatures (*normals, 2, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.98079, 1e-4);
    EXPECT_NEAR (pcy, -0.04019, 1e-4);
    EXPECT_NEAR (pcz, 0.19086, 1e-4);
    EXPECT_NEAR (pc1, 0.27207490801811218, 1e-4);
    EXPECT_NEAR (pc2, 0.19464978575706482, 1e-4);

    int indices_size = static_cast<int> (indices.size ());
    pc.computePointPrincipalCurvatures (*normals, indices_size - 3, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.86725, 1e-4);
    EXPECT_NEAR (pcy, -0.37599, 1e-4);
    EXPECT_NEAR (pcz, 0.32635, 1e-4);
    EXPECT_NEAR (pc1, 0.25900053977966309, 1e-4);
    EXPECT_NEAR (pc2, 0.17906945943832397, 1e-4);

    pc.computePointPrincipalCurvatures (*normals, indices_size - 1, indices, pcx, pcy, pcz, pc1, pc2);
    EXPECT_NEAR (pcx, 0.86725, 1e-4);
    EXPECT_NEAR (pcy, -0.375851, 1e-3);
    EXPECT_NEAR (pcz, 0.32636, 1e-4);
    EXPECT_NEAR (pc1, 0.2590005099773407,  1e-4);
    EXPECT_NEAR (pc2, 0.17906956374645233, 1e-4);

    // Object
    PointCloud<PrincipalCurvatures>::Ptr pcs (new PointCloud<PrincipalCurvatures> ());

    // set parameters
    pc.setInputCloud (cloud.makeShared ());
    pc.setIndices (indicesptr);
    pc.setSearchMethod (tree);
    pc.setKSearch (indices_size);

    // estimate
    pc.compute (*pcs);
    EXPECT_EQ (pcs->points.size (), indices.size ());

    // Adjust for small numerical inconsitencies (due to nn_indices not being sorted)
    EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[0]), 0.98509, 1e-4);
    EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[1]), 0.10713, 1e-4);
    EXPECT_NEAR (fabs (pcs->points[0].principal_curvature[2]), 0.13462, 1e-4);
    EXPECT_NEAR (fabs (pcs->points[0].pc1), 0.23997458815574646, 1e-4);
    EXPECT_NEAR (fabs (pcs->points[0].pc2), 0.19400238990783691, 1e-4);

    EXPECT_NEAR (pcs->points[2].principal_curvature[0], 0.98079, 1e-4);
    EXPECT_NEAR (pcs->points[2].principal_curvature[1], -0.04019, 1e-4);
    EXPECT_NEAR (pcs->points[2].principal_curvature[2], 0.19086, 1e-4);
    EXPECT_NEAR (pcs->points[2].pc1, 0.27207502722740173, 1e-4);
    EXPECT_NEAR (pcs->points[2].pc2, 0.1946497857570648,  1e-4);

    EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[0], 0.86725, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[1], -0.37599, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 3].principal_curvature[2], 0.32636, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 3].pc1, 0.2590007483959198,  1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 3].pc2, 0.17906941473484039, 1e-4);

    EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[0], 0.86725, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[1], -0.375851, 1e-3);
    EXPECT_NEAR (pcs->points[indices.size () - 1].principal_curvature[2], 0.32636, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 1].pc1, 0.25900065898895264, 1e-4);
    EXPECT_NEAR (pcs->points[indices.size () - 1].pc2, 0.17906941473484039, 1e-4);
}
Exemple #17
0
void segment(PointCloud<PointXYZRGB> cloud)
{
  CloudT object_cloud = get_object_points(cloud);

  objects_pub.publish(object_cloud);

  // Estimate point normals
  //  PointCloud<Normal> cloud_normals;
  //  KdTreeANN<PointXYZRGB>::Ptr tree = boost::make_shared<KdTreeANN<PointXYZRGB> >();
  //  NormalEstimation<PointXYZRGB, Normal> ne;
  //  ne.setSearchMethod(tree);
  //  ne.setInputCloud(boost::make_shared<PointCloud<PointXYZRGB> >(object_cloud));
  //  ne.setKSearch(50);
  //  ne.compute(cloud_normals);

  // Extract clusters of points (i.e., objects)
  EuclideanClusterExtraction<PointXYZRGB> clustering;
  KdTreeANN<PointXYZRGB>::Ptr cluster_tree = boost::make_shared<KdTreeANN<PointXYZRGB> >();
  vector<PointIndices> clusters;
  clustering.setInputCloud(boost::make_shared<PointCloud<PointXYZRGB> >(object_cloud));
  clustering.setClusterTolerance(0.05); // ?
  clustering.setMinClusterSize(300);
  clustering.setSearchMethod(cluster_tree); // Not sure if this should be ANN, or FLANN, or if it matters
  clustering.extract(clusters);

  //  cout << (cloud_normals.height * cloud_normals.width) << " " << (object_cloud.height * object_cloud.width) << endl;

  cout << "FOUND " << clusters.size() << " OBJECTS" << endl;
  for (uint i = 0; i < clusters.size(); i++)
  {
    cout << "CLUSTER " << i << " has " << clusters[i].indices.size() << " points" << endl;

    PointCloud<PointXYZRGB> cluster_points;
    ExtractIndices<PointXYZRGB> extract_cluster;
    extract_cluster.setInputCloud(boost::make_shared<PointCloud<PointXYZRGB> >(object_cloud));
    extract_cluster.setIndices(boost::make_shared<PointIndices>(clusters[i]));
    extract_cluster.filter(cluster_points);

    cluster_pub.publish(cluster_points);

    // Estimate point normals
    PointCloud<Normal> cluster_normals;
    KdTreeANN<PointT>::Ptr tree = boost::make_shared<KdTreeANN<PointT> >();
    // Estimate point normals
    NormalEstimation<PointT, Normal> ne;
    ne.setSearchMethod(tree);
    ne.setInputCloud(boost::make_shared<pcl::PointCloud<PointT> >(cluster_points));
    ne.setKSearch(10);
    ne.compute(cluster_normals);

    //    NormalEstimation<PointT, Normal> ne;
    //    ne.setSearchMethod(tree);
    //    ne.setInputCloud(boost::make_shared<CloudT>(cluster_points));
    //    ne.setKSearch(50);
    //    ne.compute(cluster_normals);

    //    cout << cluster_points.height * cluster_points.width << " " << cluster_normals.height * cluster_normals.width
    //        << endl;

    // Obtain the plane inliers and coefficients
    ModelCoefficients coefficients_plane;
    PointIndices inliers_plane;
    SACSegmentationFromNormals<PointT, Normal> seg_plane;
    seg_plane.setOptimizeCoefficients(true);
    seg_plane.setMethodType(SAC_RANSAC);
    seg_plane.setModelType(SACMODEL_NORMAL_PLANE);
    seg_plane.setDistanceThreshold(0.05);
    seg_plane.setInputCloud(boost::make_shared<CloudT>(cluster_points));
    seg_plane.setInputNormals(boost::make_shared<PointCloud<Normal> >(cluster_normals));
    seg_plane.segment(inliers_plane, coefficients_plane);

    //    cout << "Plane coefficients: " << coefficients_plane << endl;
    cout << "PLANE INLIERS: " << inliers_plane.indices.size() << endl;

    // Obtain the Sphere inliers and coefficients
    ModelCoefficients coefficients_sphere;
    PointIndices inliers_sphere;
    SACSegmentation<PointXYZRGB> seg_sphere;
    seg_sphere.setOptimizeCoefficients(true);
    seg_sphere.setMethodType(SAC_RANSAC);
    seg_sphere.setModelType(SACMODEL_SPHERE);
    seg_sphere.setRadiusLimits(0.01, 0.1);
    seg_sphere.setDistanceThreshold(0.005);
    seg_sphere.setInputCloud(boost::make_shared<CloudT>(cluster_points));
    seg_sphere.segment(inliers_sphere, coefficients_sphere);

    //    cout << "Sphere coefficients: " << coefficients_sphere << endl;
    cout << "SPHERE INLIERS: " << inliers_sphere.indices.size() << endl;

    ModelCoefficients coefficients_cylinder;
    PointIndices inliers_cylinder;
    SACSegmentationFromNormals<PointT, Normal> seg_cylinder;
    seg_cylinder.setOptimizeCoefficients(true);
    seg_cylinder.setModelType(SACMODEL_CYLINDER);
    seg_cylinder.setMethodType(SAC_RANSAC);
    seg_cylinder.setNormalDistanceWeight(0.1);
    seg_cylinder.setMaxIterations(1000);
    seg_cylinder.setDistanceThreshold(0.05);
    seg_cylinder.setRadiusLimits(0.01, 0.1);
    seg_cylinder.setInputCloud(boost::make_shared<CloudT>(cluster_points));
    seg_cylinder.setInputNormals(boost::make_shared<PointCloud<Normal> >(cluster_normals));
    seg_cylinder.segment(inliers_cylinder, coefficients_cylinder);

    cout << "CYLINDER INLIERS: " << inliers_cylinder.indices.size() << endl;
    cout << "Cylinder coefficients: " << coefficients_cylinder << endl;

    cout << endl;

    if (!inliers_sphere.indices.empty())
    {
      visualization_msgs::Marker marker;
      marker.header.frame_id = coefficients_sphere.header.frame_id;
      marker.header.stamp = ros::Time::now();
      marker.ns = "basic_shapes" + boost::lexical_cast<string>(i);
      marker.id = 0;
      marker.type = visualization_msgs::Marker::SPHERE;
      marker.action = visualization_msgs::Marker::ADD;
      marker.pose.position.x = coefficients_sphere.values[0];
      marker.pose.position.y = coefficients_sphere.values[1];
      marker.pose.position.z = coefficients_sphere.values[2];
      marker.pose.orientation.x = 0.0;
      marker.pose.orientation.y = 0.0;
      marker.pose.orientation.z = 0.0;
      marker.pose.orientation.w = 1.0;
      marker.scale.x = coefficients_sphere.values[3] * 2;
      marker.scale.y = coefficients_sphere.values[3] * 2;
      marker.scale.z = coefficients_sphere.values[3] * 2;
      marker.color.r = 0.0f;
      marker.color.g = 1.0f;
      marker.color.b = 0.0f;
      marker.color.a = 1.0;
      marker.lifetime = ros::Duration();
      marker_pub.publish(marker);
    }

    if (!inliers_cylinder.indices.empty())
    {
      // TODO: The cylinder coefficients are not as straightforward, need to do some
      // math to pull out the location and angle (from axis orientation and point on axis)
//      visualization_msgs::Marker marker;
//      marker.header.frame_id = coefficients_sphere.header.frame_id;
//      marker.header.stamp = ros::Time::now();
//      marker.ns = "basic_shapes" + boost::lexical_cast<string>(i);
//      marker.id = 0;
//      marker.type = visualization_msgs::Marker::SPHERE;
//      marker.action = visualization_msgs::Marker::ADD;
//      marker.pose.position.x = coefficients_sphere.values[0];
//      marker.pose.position.y = coefficients_sphere.values[1];
//      marker.pose.position.z = coefficients_sphere.values[2];
//      marker.pose.orientation.x = 0.0;
//      marker.pose.orientation.y = 0.0;
//      marker.pose.orientation.z = 0.0;
//      marker.pose.orientation.w = 1.0;
//      marker.scale.x = coefficients_sphere.values[3] * 2;
//      marker.scale.y = coefficients_sphere.values[3] * 2;
//      marker.scale.z = coefficients_sphere.values[3] * 2;
//      marker.color.r = 0.0f;
//      marker.color.g = 1.0f;
//      marker.color.b = 0.0f;
//      marker.color.a = 1.0;
//      marker.lifetime = ros::Duration();
//      marker_pub.publish(marker);
    }
  }
}
TEST (PCL, PFHEstimation)
{
  float f1, f2, f3, f4;

  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  // estimate
  n.compute (*normals);

  PFHEstimation<PointXYZ, Normal, PFHSignature125> pfh;
  pfh.setInputNormals (normals);
  EXPECT_EQ (pfh.getInputNormals (), normals);

  // computePairFeatures
  pfh.computePairFeatures (cloud, *normals, 0, 12, f1, f2, f3, f4);
  EXPECT_NEAR (f1, -0.072575, 1e-4);
  EXPECT_NEAR (f2, -0.040221, 1e-4);
  EXPECT_NEAR (f3, 0.068133, 1e-4);
  EXPECT_NEAR (f4, 0.006130, 1e-4);

  // computePointPFHSignature
  int nr_subdiv = 3;
  Eigen::VectorXf pfh_histogram (nr_subdiv * nr_subdiv * nr_subdiv);
  pfh.computePointPFHSignature (cloud, *normals, indices, nr_subdiv, pfh_histogram);
  EXPECT_NEAR (pfh_histogram[0],  0.932506, 1e-2);
  EXPECT_NEAR (pfh_histogram[1],  2.32429 , 1e-2);
  EXPECT_NEAR (pfh_histogram[2],  0.357477, 1e-2);
  EXPECT_NEAR (pfh_histogram[3],  0.848541, 1e-2);
  EXPECT_NEAR (pfh_histogram[4],  3.65565 , 2e-2); // larger error w.r.t. considering all point pairs (feature bins=0,1,1 where 1 is middle, so angle of 0)
  EXPECT_NEAR (pfh_histogram[5],  0.178104, 1e-2);
  EXPECT_NEAR (pfh_histogram[6],  1.45284 , 1e-2);
  EXPECT_NEAR (pfh_histogram[7],  3.60666 , 1e-2);
  EXPECT_NEAR (pfh_histogram[8],  0.298959, 1e-2);
  EXPECT_NEAR (pfh_histogram[9],  0.295143, 1e-2);
  EXPECT_NEAR (pfh_histogram[10], 2.13474 , 1e-2);
  EXPECT_NEAR (pfh_histogram[11], 0.41218 , 1e-2);
  EXPECT_NEAR (pfh_histogram[12], 0.165382, 1e-2);
  EXPECT_NEAR (pfh_histogram[13], 8.97407 , 1e-2);
  EXPECT_NEAR (pfh_histogram[14], 0.306592, 1e-2);
  EXPECT_NEAR (pfh_histogram[15], 0.455432, 1e-2);
  EXPECT_NEAR (pfh_histogram[16], 4.5977 ,  1e-2);
  EXPECT_NEAR (pfh_histogram[17], 0.393097, 1e-2);
  EXPECT_NEAR (pfh_histogram[18], 7.54668 , 1e-2);
  EXPECT_NEAR (pfh_histogram[19], 6.78336 , 1e-2);
  EXPECT_NEAR (pfh_histogram[20], 1.63858 , 1e-2);
  EXPECT_NEAR (pfh_histogram[21], 9.93842 , 1e-2);
  EXPECT_NEAR (pfh_histogram[22], 18.4947 , 2e-2); // larger error w.r.t. considering all point pairs (feature bins=2,1,1 where 1 is middle, so angle of 0)
  EXPECT_NEAR (pfh_histogram[23], 1.96553 , 1e-4);
  EXPECT_NEAR (pfh_histogram[24], 8.04793 , 1e-4);
  EXPECT_NEAR (pfh_histogram[25], 11.2793  , 1e-4);
  EXPECT_NEAR (pfh_histogram[26], 2.91714 , 1e-4);

  // Sum of values should be 100
  EXPECT_NEAR (pfh_histogram.sum (), 100.0, 1e-2);
  //std::cerr << pfh_histogram << std::endl;

  // Object
  PointCloud<PFHSignature125>::Ptr pfhs (new PointCloud<PFHSignature125> ());

  // set parameters
  pfh.setInputCloud (cloud.makeShared ());
  pfh.setIndices (indicesptr);
  pfh.setSearchMethod (tree);
  pfh.setKSearch (static_cast<int> (indices.size ()));

  // estimate
  pfh.compute (*pfhs);
  EXPECT_EQ (pfhs->points.size (), indices.size ());

  for (size_t i = 0; i < pfhs->points.size (); ++i)
  {
    EXPECT_NEAR (pfhs->points[i].histogram[0],  0.156477  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[1],  0.539396  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[2],  0.410907  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[3],  0.184465  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[4],  0.115767  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[5],  0.0572475 , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[6],  0.206092  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[7],  0.339667  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[8],  0.265883  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[9],  0.0038165 , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[10], 0.103046  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[11], 0.214997  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[12], 0.398186  , 3e-2); // larger error w.r.t. considering all point pairs (feature bins=0,2,2 where 2 is middle, so angle of 0)
    EXPECT_NEAR (pfhs->points[i].histogram[13], 0.298959  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[14], 0.00127217, 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[15], 0.11704   , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[16], 0.255706  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[17], 0.356205  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[18], 0.265883  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[19], 0.00127217, 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[20], 0.148844  , 1e-4);
    //EXPECT_NEAR (pfhs->points[i].histogram[21], 0.721316  , 1e-3);
    //EXPECT_NEAR (pfhs->points[i].histogram[22], 0.438899  , 1e-2);
    EXPECT_NEAR (pfhs->points[i].histogram[23], 0.22263   , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[24], 0.0216269 , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[25], 0.223902  , 1e-4);
    EXPECT_NEAR (pfhs->points[i].histogram[26], 0.07633   , 1e-4);
  }
  //Eigen::Map<Eigen::VectorXf> h (&(pfhs->points[0].histogram[0]), 125);
  //std::cerr << h.head<27> () << std::endl;

  // Test results when setIndices and/or setSearchSurface are used

  boost::shared_ptr<vector<int> > test_indices (new vector<int> (0));
  for (size_t i = 0; i < cloud.size (); i+=3)
    test_indices->push_back (static_cast<int> (i));

  testIndicesAndSearchSurface<PFHEstimation<PointXYZ, Normal, PFHSignature125>, PointXYZ, Normal, PFHSignature125>
  (cloud.makeShared (), normals, test_indices, 125);
}
TEST (PCL, NormalEstimation)
{
  Eigen::Vector4f plane_parameters;
  float curvature;

  NormalEstimation<PointXYZ, Normal> n;

  // computePointNormal (indices, Vector)
  computePointNormal (cloud, indices, plane_parameters, curvature);
  EXPECT_NEAR (fabs (plane_parameters[0]), 0.035592, 1e-4);
  EXPECT_NEAR (fabs (plane_parameters[1]), 0.369596, 1e-4);
  EXPECT_NEAR (fabs (plane_parameters[2]), 0.928511, 1e-4);
  EXPECT_NEAR (fabs (plane_parameters[3]), 0.0622552, 1e-4);
  EXPECT_NEAR (curvature, 0.0693136, 1e-4);

  float nx, ny, nz;
  // computePointNormal (indices)
  n.computePointNormal (cloud, indices, nx, ny, nz, curvature);
  EXPECT_NEAR (fabs (nx), 0.035592, 1e-4);
  EXPECT_NEAR (fabs (ny), 0.369596, 1e-4);
  EXPECT_NEAR (fabs (nz), 0.928511, 1e-4);
  EXPECT_NEAR (curvature, 0.0693136, 1e-4);

  // computePointNormal (Vector)
  computePointNormal (cloud, plane_parameters, curvature);
  EXPECT_NEAR (plane_parameters[0],  0.035592,  1e-4);
  EXPECT_NEAR (plane_parameters[1],  0.369596,  1e-4);
  EXPECT_NEAR (plane_parameters[2],  0.928511,  1e-4);
  EXPECT_NEAR (plane_parameters[3], -0.0622552, 1e-4);
  EXPECT_NEAR (curvature,            0.0693136, 1e-4);

  // flipNormalTowardsViewpoint (Vector)
  flipNormalTowardsViewpoint (cloud.points[0], 0, 0, 0, plane_parameters);
  EXPECT_NEAR (plane_parameters[0], -0.035592,  1e-4);
  EXPECT_NEAR (plane_parameters[1], -0.369596,  1e-4);
  EXPECT_NEAR (plane_parameters[2], -0.928511,  1e-4);
  EXPECT_NEAR (plane_parameters[3],  0.0799743, 1e-4);

  // flipNormalTowardsViewpoint
  flipNormalTowardsViewpoint (cloud.points[0], 0, 0, 0, nx, ny, nz);
  EXPECT_NEAR (nx, -0.035592, 1e-4);
  EXPECT_NEAR (ny, -0.369596, 1e-4);
  EXPECT_NEAR (nz, -0.928511, 1e-4);

  // Object
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());

  // set parameters
  PointCloud<PointXYZ>::Ptr cloudptr = cloud.makeShared ();
  n.setInputCloud (cloudptr);
  EXPECT_EQ (n.getInputCloud (), cloudptr);
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  EXPECT_EQ (n.getIndices (), indicesptr);
  n.setSearchMethod (tree);
  EXPECT_EQ (n.getSearchMethod (), tree);
  n.setKSearch (static_cast<int> (indices.size ()));

  // estimate
  n.compute (*normals);
  EXPECT_EQ (normals->points.size (), indices.size ());

  for (size_t i = 0; i < normals->points.size (); ++i)
  {
    EXPECT_NEAR (normals->points[i].normal[0], -0.035592, 1e-4);
    EXPECT_NEAR (normals->points[i].normal[1], -0.369596, 1e-4);
    EXPECT_NEAR (normals->points[i].normal[2], -0.928511, 1e-4);
    EXPECT_NEAR (normals->points[i].curvature, 0.0693136, 1e-4);
  }

  PointCloud<PointXYZ>::Ptr surfaceptr = cloudptr;
  n.setSearchSurface (surfaceptr);
  EXPECT_EQ (n.getSearchSurface (), surfaceptr);

  // Additional test for searchForNeigbhors
  surfaceptr.reset (new PointCloud<PointXYZ>);
  *surfaceptr = *cloudptr;
  surfaceptr->points.resize (640 * 480);
  surfaceptr->width = 640;
  surfaceptr->height = 480;
  EXPECT_EQ (surfaceptr->points.size (), surfaceptr->width * surfaceptr->height);
  n.setSearchSurface (surfaceptr);
  tree.reset ();
  n.setSearchMethod (tree);

  // estimate
  n.compute (*normals);
  EXPECT_EQ (normals->points.size (), indices.size ());
}
Exemple #20
0
/* ---[ */
int
main (int argc, char** argv)
{
  if (argc < 2)
  {
    std::cerr << "No test file given. Please download `bun0.pcd` and pass its path to the test." << std::endl;
    return (-1);
  }

  // Load file
  sensor_msgs::PointCloud2 cloud_blob;
  loadPCDFile (argv[1], cloud_blob);
  fromROSMsg (cloud_blob, *cloud);

  // Create search tree
  tree.reset (new search::KdTree<PointXYZ> (false));
  tree->setInputCloud (cloud);

  // Normal estimation
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  n.setInputCloud (cloud);
  //n.setIndices (indices[B);
  n.setSearchMethod (tree);
  n.setKSearch (20);
  n.compute (*normals);

  // Concatenate XYZ and normal information
  pcl::concatenateFields (*cloud, *normals, *cloud_with_normals);
      
  // Create search tree
  tree2.reset (new search::KdTree<PointNormal>);
  tree2->setInputCloud (cloud_with_normals);

  // Process for update cloud
  if(argc == 3){
    sensor_msgs::PointCloud2 cloud_blob1;
    loadPCDFile (argv[2], cloud_blob1);
    fromROSMsg (cloud_blob1, *cloud1);
        // Create search tree
    tree3.reset (new search::KdTree<PointXYZ> (false));
    tree3->setInputCloud (cloud1);

    // Normal estimation
    NormalEstimation<PointXYZ, Normal> n1;
    PointCloud<Normal>::Ptr normals1 (new PointCloud<Normal> ());
    n1.setInputCloud (cloud1);

    n1.setSearchMethod (tree3);
    n1.setKSearch (20);
    n1.compute (*normals1);

    // Concatenate XYZ and normal information
    pcl::concatenateFields (*cloud1, *normals1, *cloud_with_normals1);
    // Create search tree
    tree4.reset (new search::KdTree<PointNormal>);
    tree4->setInputCloud (cloud_with_normals1);
  }

  // Testing
  testing::InitGoogleTest (&argc, argv);
  return (RUN_ALL_TESTS ());
}
TEST (PCL, FPFHEstimation)
{
  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  // estimate
  n.compute (*normals);

  FPFHEstimation<PointXYZ, Normal, FPFHSignature33> fpfh;
  fpfh.setInputNormals (normals);
  EXPECT_EQ (fpfh.getInputNormals (), normals);

  // computePointSPFHSignature
  int nr_subdiv = 11; // use the same number of bins for all three angular features
  Eigen::MatrixXf hist_f1 (indices.size (), nr_subdiv), hist_f2 (indices.size (), nr_subdiv), hist_f3 (indices.size (), nr_subdiv);
  hist_f1.setZero (); hist_f2.setZero (); hist_f3.setZero ();
  for (int i = 0; i < static_cast<int> (indices.size ()); ++i)
    fpfh.computePointSPFHSignature (cloud, *normals, i, i, indices, hist_f1, hist_f2, hist_f3);

  EXPECT_NEAR (hist_f1 (0, 0), 0.757576, 1e-4);
  EXPECT_NEAR (hist_f1 (0, 1), 0.757576, 1e-4);
  EXPECT_NEAR (hist_f1 (0, 2), 4.54545,  1e-4);
  EXPECT_NEAR (hist_f1 (0, 3), 19.697,   1e-4);
  EXPECT_NEAR (hist_f1 (0, 4), 40.6566,  1e-4);
  EXPECT_NEAR (hist_f1 (0, 5), 21.4647,  1e-4);
  EXPECT_NEAR (hist_f1 (0, 6), 7.575759, 1e-4);
  EXPECT_NEAR (hist_f1 (0, 7), 0.000000, 1e-4);
  EXPECT_NEAR (hist_f1 (0, 8), 0.000000, 1e-4);
  EXPECT_NEAR (hist_f1 (0, 9), 0.50505,  1e-4);
  EXPECT_NEAR (hist_f1 (0, 10), 4.0404,  1e-4);

  EXPECT_NEAR (hist_f2 (0, 0), 0.757576, 1e-4);
  EXPECT_NEAR (hist_f2 (0, 1), 1.51515,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 2), 6.31313,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 3), 9.59596,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 4), 20.7071,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 5), 18.9394,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 6), 15.9091,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 7), 12.8788,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 8), 6.56566,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 9), 4.29293,  1e-4);
  EXPECT_NEAR (hist_f2 (0, 10), 2.52525, 1e-4);

  EXPECT_NEAR (hist_f3 (0, 0), 0.000000, 1e-4);
  EXPECT_NEAR (hist_f3 (0, 1), 5.05051,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 2), 4.54545,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 3), 5.05051,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 4), 1.76768,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 5), 3.0303,   1e-4);
  EXPECT_NEAR (hist_f3 (0, 6), 9.09091,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 7), 31.8182,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 8), 22.2222,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 9), 11.8687,  1e-4);
  EXPECT_NEAR (hist_f3 (0, 10), 5.55556, 1e-4);

  // weightPointSPFHSignature
  Eigen::VectorXf fpfh_histogram (nr_subdiv + nr_subdiv + nr_subdiv);
  fpfh_histogram.setZero ();
  vector<float> dists (indices.size ());
  for (size_t i = 0; i < dists.size (); ++i) dists[i] = static_cast<float> (i);
  fpfh.weightPointSPFHSignature (hist_f1, hist_f2, hist_f3, indices, dists, fpfh_histogram);

  EXPECT_NEAR (fpfh_histogram[0],  1.9798 ,  1e-2);
  EXPECT_NEAR (fpfh_histogram[1],  2.86927,  1e-2);
  EXPECT_NEAR (fpfh_histogram[2],  8.47911,  1e-2);
  EXPECT_NEAR (fpfh_histogram[3],  22.8784,  1e-2);
  EXPECT_NEAR (fpfh_histogram[4],  29.8597,  1e-2);
  EXPECT_NEAR (fpfh_histogram[5],  19.6877,  1e-2);
  EXPECT_NEAR (fpfh_histogram[6],  7.38611,  1e-2);
  EXPECT_NEAR (fpfh_histogram[7],  1.44265,  1e-2);
  EXPECT_NEAR (fpfh_histogram[8],  0.69677,  1e-2);
  EXPECT_NEAR (fpfh_histogram[9],  1.72609,  1e-2);
  EXPECT_NEAR (fpfh_histogram[10], 2.99435,  1e-2);
  EXPECT_NEAR (fpfh_histogram[11], 2.26313,  1e-2);
  EXPECT_NEAR (fpfh_histogram[12], 5.16573,  1e-2);
  EXPECT_NEAR (fpfh_histogram[13], 8.3263 ,  1e-2);
  EXPECT_NEAR (fpfh_histogram[14], 9.92427,  1e-2);
  EXPECT_NEAR (fpfh_histogram[15], 16.8062,  1e-2);
  EXPECT_NEAR (fpfh_histogram[16], 16.2767,  1e-2);
  EXPECT_NEAR (fpfh_histogram[17], 12.251 ,  1e-2);
  //EXPECT_NEAR (fpfh_histogram[18], 10.354,  1e-1);
  //EXPECT_NEAR (fpfh_histogram[19], 6.65578,  1e-2);
  EXPECT_NEAR (fpfh_histogram[20], 6.1437 ,  1e-2);
  EXPECT_NEAR (fpfh_histogram[21], 5.83341,  1e-2);
  EXPECT_NEAR (fpfh_histogram[22], 1.08809,  1e-2);
  EXPECT_NEAR (fpfh_histogram[23], 3.34133,  1e-2);
  EXPECT_NEAR (fpfh_histogram[24], 5.59236,  1e-2);
  EXPECT_NEAR (fpfh_histogram[25], 5.6355 ,  1e-2);
  EXPECT_NEAR (fpfh_histogram[26], 3.03257,  1e-2);
  EXPECT_NEAR (fpfh_histogram[27], 1.37437,  1e-2);
  EXPECT_NEAR (fpfh_histogram[28], 7.99746,  1e-2);
  EXPECT_NEAR (fpfh_histogram[29], 18.0343,  1e-2);
  EXPECT_NEAR (fpfh_histogram[30], 23.691 ,  1e-2);
  EXPECT_NEAR (fpfh_histogram[31], 19.8475,  1e-2);
  EXPECT_NEAR (fpfh_histogram[32], 10.3655,  1e-2);

  // Object
  PointCloud<FPFHSignature33>::Ptr fpfhs (new PointCloud<FPFHSignature33> ());

  // set parameters
  fpfh.setInputCloud (cloud.makeShared ());
  fpfh.setNrSubdivisions (11, 11, 11);
  fpfh.setIndices (indicesptr);
  fpfh.setSearchMethod (tree);
  fpfh.setKSearch (static_cast<int> (indices.size ()));

  // estimate
  fpfh.compute (*fpfhs);
  EXPECT_EQ (fpfhs->points.size (), indices.size ());

  EXPECT_NEAR (fpfhs->points[0].histogram[0],  1.58591, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[1],  1.68365, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[2],  6.71   , 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[3],  23.0717, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[4],  33.3844, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[5],  20.4002, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[6],  7.31067, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[7],  1.02635, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[8],  0.48591, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[9],  1.47069, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[10], 2.87061, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[11], 1.78321, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[12], 4.30795, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[13], 7.05514, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[14], 9.37615, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[15], 17.963 , 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[16], 18.2801, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[17], 14.2766, 1e-2);
  //EXPECT_NEAR (fpfhs->points[0].histogram[18], 10.8542, 1e-2);
  //EXPECT_NEAR (fpfhs->points[0].histogram[19], 6.07925, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[20], 5.28565, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[21], 4.73887, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[22], 0.56984, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[23], 3.29826, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[24], 5.28156, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[25], 5.26939, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[26], 3.13191, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[27], 1.74453, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[28], 9.41971, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[29], 21.5894, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[30], 24.6302, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[31], 17.7764, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[32], 7.28878, 1e-2);

  // Test results when setIndices and/or setSearchSurface are used

  boost::shared_ptr<vector<int> > test_indices (new vector<int> (0));
  for (size_t i = 0; i < cloud.size (); i+=3)
    test_indices->push_back (static_cast<int> (i));

  testIndicesAndSearchSurface<FPFHEstimation<PointXYZ, Normal, FPFHSignature33>, PointXYZ, Normal, FPFHSignature33>
  (cloud.makeShared (), normals, test_indices, 33);

}
TEST (PCL, FPFHEstimationOpenMP)
{
  // Estimate normals first
  NormalEstimation<PointXYZ, Normal> n;
  PointCloud<Normal>::Ptr normals (new PointCloud<Normal> ());
  // set parameters
  n.setInputCloud (cloud.makeShared ());
  boost::shared_ptr<vector<int> > indicesptr (new vector<int> (indices));
  n.setIndices (indicesptr);
  n.setSearchMethod (tree);
  n.setKSearch (10); // Use 10 nearest neighbors to estimate the normals
  // estimate
  n.compute (*normals);
  FPFHEstimationOMP<PointXYZ, Normal, FPFHSignature33> fpfh (4); // instantiate 4 threads
  fpfh.setInputNormals (normals);

  // Object
  PointCloud<FPFHSignature33>::Ptr fpfhs (new PointCloud<FPFHSignature33> ());

  // set parameters
  fpfh.setInputCloud (cloud.makeShared ());
  fpfh.setNrSubdivisions (11, 11, 11);
  fpfh.setIndices (indicesptr);
  fpfh.setSearchMethod (tree);
  fpfh.setKSearch (static_cast<int> (indices.size ()));

  // estimate
  fpfh.compute (*fpfhs);
  EXPECT_EQ (fpfhs->points.size (), indices.size ());

  EXPECT_NEAR (fpfhs->points[0].histogram[0],  1.58591, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[1],  1.68365, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[2],  6.71   , 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[3],  23.073, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[4],  33.3828, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[5],  20.4002, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[6],  7.31067, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[7],  1.02635, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[8],  0.48591, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[9],  1.47069, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[10], 2.87061, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[11], 1.78321, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[12], 4.30795, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[13], 7.05514, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[14], 9.37615, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[15], 17.963 , 1e-3);
  //EXPECT_NEAR (fpfhs->points[0].histogram[16], 18.2801, 1e-3);
  //EXPECT_NEAR (fpfhs->points[0].histogram[17], 14.2766, 1e-3);
  //EXPECT_NEAR (fpfhs->points[0].histogram[18], 10.8542, 1e-3);
  //EXPECT_NEAR (fpfhs->points[0].histogram[19], 6.07925, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[20], 5.28991, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[21], 4.73438, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[22], 0.56984, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[23], 3.29826, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[24], 5.28156, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[25], 5.26939, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[26], 3.13191, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[27], 1.74453, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[28], 9.41971, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[29], 21.5894, 1e-2);
  EXPECT_NEAR (fpfhs->points[0].histogram[30], 24.6302, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[31], 17.7764, 1e-3);
  EXPECT_NEAR (fpfhs->points[0].histogram[32], 7.28878, 1e-3);

  // Test results when setIndices and/or setSearchSurface are used

  boost::shared_ptr<vector<int> > test_indices (new vector<int> (0));
  for (size_t i = 0; i < cloud.size (); i+=3)
    test_indices->push_back (static_cast<int> (i));

  testIndicesAndSearchSurface<FPFHEstimationOMP<PointXYZ, Normal, FPFHSignature33>, PointXYZ, Normal, FPFHSignature33>
  (cloud.makeShared (), normals, test_indices, 33);
}