Class List
Here are the classes, structs, unions and interfaces with brief descriptions:
pcl_ros::BAGReaderBAG PointCloud file format reader
pcl_ros::BasePublisher
pcl_ros::BoundaryEstimationBoundaryEstimation 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::ConvexHull2DConvexHull2D represents a 2D ConvexHull implementation
ros::message_traits::DataType< pcl::PointCloud< T > >
ros::message_traits::Definition< pcl::PointCloud< T > >
pcl_ros::EuclideanClusterExtractionEuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense
pcl_ros::ExtractIndicesExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud
pcl_ros::ExtractPolygonalPrismDataExtractPolygonalPrismData 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::FeatureFeature 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::FilterFilter represents the base filter class. Some generic 3D operations that are applicable to all filters are defined here as static methods
pcl_ros::FPFHEstimationFPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset containing points and normals
pcl_ros::FPFHEstimationOMPFPFHEstimationOMP 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::MomentInvariantsEstimationMomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point
pcl_ros::MovingLeastSquaresMovingLeastSquares 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::NormalEstimationNormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures
pcl_ros::NormalEstimationOMPNormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures, in parallel, using the OpenMP standard
pcl_ros::NormalEstimationTBBNormalEstimationTBB 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::PassThroughPassThrough uses the base Filter class methods to pass through all data that satisfies the user given constraints
PCDGenerator
pcl_ros::PCDReaderPoint Cloud Data (PCD) file format reader
pcl_ros::PCDWriterPoint Cloud Data (PCD) file format writer
pcl_ros::PCLNodeletPCLNodelet represents the base PCL Nodelet class. All PCL nodelets should inherit from this class
pcl_ros::PFHEstimationPFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing points and normals
pcl_ros::PointCloudConcatenateDataSynchronizerPointCloudConcatenateFieldsSynchronizer 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::PointCloudConcatenateFieldsSynchronizerPointCloudConcatenateFieldsSynchronizer 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::PrincipalCurvaturesEstimationPrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface curvatures for a given point cloud dataset containing points and normals
pcl_ros::ProjectInliersProjectInliers 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::RadiusOutlierRemovalRadiusOutlierRemoval 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::SACSegmentationSACSegmentation 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::SACSegmentationFromNormalsSACSegmentationFromNormals represents the PCL nodelet segmentation class for Sample Consensus methods and models that require the use of surface normals for estimation
pcl_ros::SegmentDifferencesSegmentDifferences 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::StatisticalOutlierRemovalStatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data. For more information check:
pcl_ros::TestListener
pcl_ros::TestPingPong
pcl_ros::TestTalker
pcl_ros::VFHEstimationVFHEstimation estimates the Viewpoint Feature Histogram (VFH) descriptor for a given point cloud dataset containing points and normals
pcl_ros::VoxelGridVoxelGrid assembles a local 3D grid over a given PointCloud, and downsamples + filters the data
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pcl_ros
Author(s): Maintained by Radu Bogdan Rusu
autogenerated on Tue Mar 5 2013 13:54:41