Пример #1
0
template <typename PointT> void
pcl::getPointCloudDifference (
    const pcl::PointCloud<PointT> &src, 
    const pcl::PointCloud<PointT> &, 
    double threshold, const boost::shared_ptr<pcl::search::Search<PointT> > &tree,
    pcl::PointCloud<PointT> &output)
{
  // We're interested in a single nearest neighbor only
  std::vector<int> nn_indices (1);
  std::vector<float> nn_distances (1);

  // The src indices that do not have a neighbor in tgt
  std::vector<int> src_indices;

  // Iterate through the source data set
  for (int i = 0; i < static_cast<int> (src.points.size ()); ++i)
  {
    if (!isFinite (src.points[i]))
      continue;
    // Search for the closest point in the target data set (number of neighbors to find = 1)
    if (!tree->nearestKSearch (src.points[i], 1, nn_indices, nn_distances))
    {
      PCL_WARN ("No neighbor found for point %zu (%f %f %f)!\n", i, src.points[i].x, src.points[i].y, src.points[i].z);
      continue;
    }

    if (nn_distances[0] > threshold)
      src_indices.push_back (i);
  }
 
  // Allocate enough space and copy the basics
  output.points.resize (src_indices.size ());
  output.header   = src.header;
  output.width    = static_cast<uint32_t> (src_indices.size ());
  output.height   = 1;
  //if (src.is_dense)
    output.is_dense = true;
  //else
    // It's not necessarily true that is_dense is false if cloud_in.is_dense is false
    // To verify this, we would need to iterate over all points and check for NaNs
    //output.is_dense = false;

  // Copy all the data fields from the input cloud to the output one
  typedef typename pcl::traits::fieldList<PointT>::type FieldList;
  // Iterate over each point
  for (size_t i = 0; i < src_indices.size (); ++i)
    // Iterate over each dimension
    pcl::for_each_type <FieldList> (NdConcatenateFunctor <PointT, PointT> (src.points[src_indices[i]], output.points[i]));
}
Пример #2
0
template <typename PointSource, typename PointTarget, typename FeatureT> void 
pcl::SampleConsensusInitialAlignment<PointSource, PointTarget, FeatureT>::findSimilarFeatures (
    const FeatureCloud &input_features, const std::vector<int> &sample_indices, 
    std::vector<int> &corresponding_indices)
{
  std::vector<int> nn_indices (k_correspondences_);
  std::vector<float> nn_distances (k_correspondences_);

  corresponding_indices.resize (sample_indices.size ());
  for (size_t i = 0; i < sample_indices.size (); ++i)
  {
    // Find the k features nearest to input_features.points[sample_indices[i]]
    feature_tree_->nearestKSearch (input_features, sample_indices[i], k_correspondences_, nn_indices, nn_distances);

    // Select one at random and add it to corresponding_indices
    int random_correspondence = getRandomIndex (k_correspondences_);
    corresponding_indices[i] = nn_indices[random_correspondence];
  }
}
Пример #3
0
template <typename PointT> void
pcl::getPointCloudDifference (
    const pcl::PointCloud<PointT> &src,
    double threshold,
    const typename pcl::search::Search<PointT>::Ptr &tree,
    pcl::PointCloud<PointT> &output)
{
  // We're interested in a single nearest neighbor only
  std::vector<int> nn_indices (1);
  std::vector<float> nn_distances (1);

  // The input cloud indices that do not have a neighbor in the target cloud
  std::vector<int> src_indices;

  // Iterate through the source data set
  for (int i = 0; i < static_cast<int> (src.points.size ()); ++i)
  {
    // Ignore invalid points in the inpout cloud
    if (!isFinite (src.points[i]))
      continue;
    // Search for the closest point in the target data set (number of neighbors to find = 1)
    if (!tree->nearestKSearch (src.points[i], 1, nn_indices, nn_distances))
    {
      PCL_WARN ("No neighbor found for point %lu (%f %f %f)!\n", i, src.points[i].x, src.points[i].y, src.points[i].z);
      continue;
    }
    // Add points without a corresponding point in the target cloud to the output cloud
    if (nn_distances[0] > threshold)
      src_indices.push_back (i);
  }

  // Copy all the data fields from the input cloud to the output one
  copyPointCloud (src, src_indices, output);

  // Output is always dense, as invalid points in the input cloud are ignored
  output.is_dense = true;
}