| pcl_ros::BAGReader | BAG PointCloud file format reader |
| pcl_ros::BasePublisher | |
| pcl_ros::BoundaryEstimation | BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The code makes use of the estimated surface normals at each point in the input dataset |
| pcl_ros::ConvexHull2D | ConvexHull2D represents a 2D ConvexHull implementation |
| ros::message_traits::DataType< pcl::PointCloud< T > > | |
| ros::message_traits::Definition< pcl::PointCloud< T > > | |
| pcl_ros::EuclideanClusterExtraction | EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense |
| pcl_ros::ExtractIndices | ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud |
| pcl_ros::ExtractPolygonalPrismData | ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it |
| pcl_ros::Feature | Feature represents the base feature class. Some generic 3D operations that are applicable to all features are defined here as static methods |
| pcl_ros::FeatureFromNormals | |
| pcl::detail::FieldsLength< PointT > | |
| pcl::detail::FieldStreamer< Stream, PointT > | |
| pcl_ros::Filter | Filter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods |
| pcl_ros::FPFHEstimation | FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals |
| pcl_ros::FPFHEstimationOMP | FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals, in parallel, using the OpenMP standard |
| ros::message_traits::HasHeader< pcl::PointCloud< T > > | |
| ros::message_traits::MD5Sum< pcl::PointCloud< T > > | |
| pcl_ros::MomentInvariantsEstimation | MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point |
| pcl_ros::MovingLeastSquares | MovingLeastSquares represents a nodelet using the MovingLeastSquares implementation. The type of the output is the same as the input, it only smooths the XYZ coordinates according to the parameters. Normals are estimated at each point as well and published on a separate topic |
| pcl_ros::NormalEstimation | NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures |
| pcl_ros::NormalEstimationOMP | NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard |
| pcl_ros::NormalEstimationTBB | NormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using Intel's Threading Building Blocks library |
| pcl_ros::PassThrough | PassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints |
| PCDGenerator | |
| pcl_ros::PCDReader | Point Cloud Data (PCD) file format reader |
| pcl_ros::PCDWriter | Point Cloud Data (PCD) file format writer |
| pcl_ros::PCLNodelet | PCLNodelet represents the base PCL Nodelet class. All PCL nodelets should inherit from this class |
| pcl_ros::PFHEstimation | PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals |
| pcl_ros::PointCloudConcatenateDataSynchronizer | PointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message |
| pcl_ros::PointCloudConcatenateFieldsSynchronizer | PointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message |
| PointCloudToImage | |
| PointCloudToPCD | |
| pcl_ros::PrincipalCurvaturesEstimation | PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals |
| pcl_ros::ProjectInliers | ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud |
| pcl_ros::Publisher< PointT > | |
| pcl_ros::Publisher< sensor_msgs::PointCloud2 > | |
| pcl_ros::RadiusOutlierRemoval | RadiusOutlierRemoval is a simple filter that removes outliers if the number of neighbors in a certain search radius is smaller than a given K |
| pcl_ros::SACSegmentation | SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation |
| pcl_ros::SACSegmentationFromNormals | SACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation |
| pcl_ros::SegmentDifferences | SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the difference between them for a maximum given distance threshold |
| ros::serialization::Serializer< pcl::PointCloud< T > > | |
| pcl_ros::StatisticalOutlierRemoval | StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check: |
| pcl_ros::VFHEstimation | VFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals |
| pcl_ros::VoxelGrid | VoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data |