Fix syntax of nodelet manifest file
By splitting files for each library. Close #131
This commit is contained in:
Kentaro Wada
Paul Bovbel
parent
909b76d773
commit
d1368c0f65
@ -48,7 +48,11 @@
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<test_depend>rostest</test_depend>
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<test_depend>rostest</test_depend>
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<export>
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<export>
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<nodelet plugin="${prefix}/pcl_nodelets.xml"/>
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<nodelet plugin="${prefix}/plugins/nodelet/libpcl_ros_features.xml"/>
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<nodelet plugin="${prefix}/plugins/nodelet/libpcl_ros_filters.xml"/>
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<nodelet plugin="${prefix}/plugins/nodelet/libpcl_ros_io.xml"/>
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<nodelet plugin="${prefix}/plugins/nodelet/libpcl_ros_segmentation.xml"/>
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<nodelet plugin="${prefix}/plugins/nodelet/libpcl_ros_surface.xml"/>
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</export>
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</export>
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</package>
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</package>
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@ -1,232 +0,0 @@
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<!-- PCL Features library component -->
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<library path="lib/libpcl_ros_features">
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<class name="pcl/BoundaryEstimation" type="BoundaryEstimation" base_class_type="nodelet::Nodelet">
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<description>
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BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The
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code makes use of the estimated surface normals at each point in the input data set.
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</description>
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</class>
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<class name="pcl/FPFHEstimation" type="FPFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset
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containing points and normals.
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</description>
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</class>
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<class name="pcl/FPFHEstimationOMP" type="FPFHEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset
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containing points and normals, in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/SHOTEstimation" type="SHOTEstimation" base_class_type="nodelet::Nodelet">
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<description>
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SHOTEstimation estimates SHOT descriptor for a given point cloud dataset
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containing points and normals.
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</description>
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</class>
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<class name="pcl/SHOTEstimationOMP" type="SHOTEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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SHOTEstimationOMP estimates SHOT descriptor for a given point cloud dataset
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containing points and normals, in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/MomentInvariantsEstimation" type="MomentInvariantsEstimation" base_class_type="nodelet::Nodelet">
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<description>
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MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point.
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</description>
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</class>
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<class name="pcl/NormalEstimationOMP" type="NormalEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures,
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in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/NormalEstimationTBB" type="NormalEstimationTBB" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in
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parallel, using Intel's Threading Building Blocks library.
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</description>
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</class>
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<class name="pcl/NormalEstimation" type="NormalEstimation" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures.
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</description>
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</class>
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<class name="pcl/PFHEstimation" type="PFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing
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points and normals.
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</description>
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</class>
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<class name="pcl/PrincipalCurvaturesEstimation" type="PrincipalCurvaturesEstimation" base_class_type="nodelet::Nodelet">
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<description>
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PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface
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curvatures for a given point cloud dataset containing points and normals.
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</description>
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</class>
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<class name="pcl/VFHEstimation" type="VFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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VFHEstimation estimates the Viewpoint Feature Histogram (VFH) global descriptor for a given point cloud cluster dataset
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containing points and normals.
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</description>
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</class>
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</library>
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<!-- PCL IO library component -->
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<library path="lib/libpcl_ros_io">
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<class name="pcl/NodeletMUX" type="NodeletMUX" base_class_type="nodelet::Nodelet">
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<description>
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NodeletMUX represent a mux nodelet for PointCloud topics: it takes N (up
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to 8) input topics, and publishes all of them on one output topic.
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</description>
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</class>
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<class name="pcl/NodeletDEMUX" type="NodeletDEMUX" base_class_type="nodelet::Nodelet">
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<description>
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NodeletDEMUX represent a demux nodelet for PointCloud topics: it
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publishes 1 input topic to N output topics.
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</description>
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</class>
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<class name="pcl/PCDReader" type="PCDReader" base_class_type="nodelet::Nodelet">
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<description>
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PCDReader reads in a PCD (Point Cloud Data) v.5 file from disk and converts it to a PointCloud message.
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</description>
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</class>
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<class name="pcl/BAGReader" type="BAGReader" base_class_type="nodelet::Nodelet">
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<description>
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BAGReader reads in sensor_msgs/PointCloud2 messages from BAG files.
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</description>
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</class>
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<class name="pcl/PCDWriter" type="PCDWriter" base_class_type="nodelet::Nodelet">
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<description>
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PCDWriter writes a PointCloud message to disk in a PCD (Point Cloud Data) v.5 file format.
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</description>
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</class>
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<class name="pcl/PointCloudConcatenateFieldsSynchronizer" type="PointCloudConcatenateFieldsSynchronizer" base_class_type="nodelet::Nodelet">
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<description>
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PointCloudConcatenateFieldsSynchronizer is a special form of data synchronizer: it listens for a set of input PointCloud messages on the
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same topic, checks their timestamps, and concatenates their fields together into a single PointCloud output message.
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</description>
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</class>
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<class name="pcl/PointCloudConcatenateDataSynchronizer" type="PointCloudConcatenateDataSynchronizer" base_class_type="nodelet::Nodelet">
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<description>
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PointCloudConcatenateDataSynchronizer is a special form of data
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synchronizer: it listens for a set of input PointCloud messages on
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different topics, and concatenates them together into a single PointCloud
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output message.
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</description>
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</class>
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</library>
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<!-- PCL Filters library component -->
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<library path="lib/libpcl_ros_filters">
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<class name="pcl/PassThrough" type="PassThrough" base_class_type="nodelet::Nodelet">
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<description>
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PassThrough is a filter that uses the basic Filter class mechanisms for passing data around.
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</description>
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</class>
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<class name="pcl/VoxelGrid" type="VoxelGrid" base_class_type="nodelet::Nodelet">
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<description>
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VoxelGrid assembles a local 3D grid over a given PointCloud, and uses that to downsample the data.
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</description>
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</class>
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<class name="pcl/ProjectInliers" type="ProjectInliers" base_class_type="nodelet::Nodelet">
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<description>
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ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud.
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</description>
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</class>
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<class name="pcl/ExtractIndices" type="ExtractIndices" base_class_type="nodelet::Nodelet">
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<description>
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ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud.
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</description>
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</class>
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<class name="pcl/StatisticalOutlierRemoval" type="StatisticalOutlierRemoval" base_class_type="nodelet::Nodelet">
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<description>
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StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
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</description>
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</class>
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<class name="pcl/RadiusOutlierRemoval" type="RadiusOutlierRemoval" base_class_type="nodelet::Nodelet">
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<description>
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RadiusOutlierRemoval uses point neighborhood statistics to filter outlier data.
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</description>
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</class>
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<class name="pcl/CropBox" type="CropBox" base_class_type="nodelet::Nodelet">
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<description>
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CropBox is a filter that allows the user to filter all the data inside of a given box.
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</description>
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</class>
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</library>
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<!-- PCL Segmentation library component -->
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<library path="lib/libpcl_ros_segmentation">
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<class name="pcl/ExtractPolygonalPrismData" type="ExtractPolygonalPrismData" base_class_type="nodelet::Nodelet">
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<description>
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ExtractPolygonalPrismData uses a set of point indices that represent a planar model, and together with a given
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height, generates a 3D polygonal prism. The polygonal prism is then used to segment all points lying inside it.
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</description>
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</class>
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<class name="pcl/EuclideanClusterExtraction" type="EuclideanClusterExtraction" base_class_type="nodelet::Nodelet">
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<description>
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EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense.
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</description>
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</class>
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<class name="pcl/SACSegmentationFromNormals" type="SACSegmentationFromNormals" base_class_type="nodelet::Nodelet">
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<description>
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SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that
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it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation.
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</description>
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</class>
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<class name="pcl/SACSegmentation" type="SACSegmentation" base_class_type="nodelet::Nodelet">
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<description>
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SACSegmentation represents the Nodelet segmentation class for Sample Consensus methods and models, in the sense that
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it just creates a Nodelet wrapper for generic-purpose SAC-based segmentation.
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</description>
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</class>
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<class name="pcl/SegmentDifferences" type="SegmentDifferences" base_class_type="nodelet::Nodelet">
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<description>
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SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the
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difference between them for a maximum given distance threshold.
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</description>
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</class>
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</library>
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<!-- PCL Surface reconstruction library component -->
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<library path="lib/libpcl_ros_surface">
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<class name="pcl/MovingLeastSquares" type="MovingLeastSquares" base_class_type="nodelet::Nodelet">
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<description>
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MovingLeastSquares is an implementation of the MLS algorithm for data reconstruction through bivariate polynomial fitting.
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</description>
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</class>
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<class name="pcl/ConvexHull2D" type="ConvexHull2D" base_class_type="nodelet::Nodelet">
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<description>
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ConvexHull2D represents a 2D ConvexHull implementation.
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</description>
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</class>
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</library>
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85
pcl_ros/plugins/nodelet/libpcl_ros_features.xml
Normal file
85
pcl_ros/plugins/nodelet/libpcl_ros_features.xml
Normal file
@ -0,0 +1,85 @@
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<!-- PCL Features library component -->
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<library path="lib/libpcl_ros_features">
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<class name="pcl/BoundaryEstimation" type="BoundaryEstimation" base_class_type="nodelet::Nodelet">
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<description>
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BoundaryEstimation estimates whether a set of points is lying on surface boundaries using an angle criterion. The
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code makes use of the estimated surface normals at each point in the input data set.
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</description>
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</class>
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<class name="pcl/FPFHEstimation" type="FPFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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FPFHEstimation estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset
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containing points and normals.
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</description>
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</class>
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<class name="pcl/FPFHEstimationOMP" type="FPFHEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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FPFHEstimationOMP estimates the Fast Point Feature Histogram (FPFH) descriptor for a given point cloud dataset
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containing points and normals, in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/SHOTEstimation" type="SHOTEstimation" base_class_type="nodelet::Nodelet">
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<description>
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SHOTEstimation estimates SHOT descriptor for a given point cloud dataset
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containing points and normals.
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</description>
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</class>
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<class name="pcl/SHOTEstimationOMP" type="SHOTEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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SHOTEstimationOMP estimates SHOT descriptor for a given point cloud dataset
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containing points and normals, in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/MomentInvariantsEstimation" type="MomentInvariantsEstimation" base_class_type="nodelet::Nodelet">
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<description>
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MomentInvariantsEstimation estimates the 3 moment invariants (j1, j2, j3) at each 3D point.
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</description>
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</class>
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<class name="pcl/NormalEstimationOMP" type="NormalEstimationOMP" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimationOMP estimates local surface properties at each 3D point, such as surface normals and curvatures,
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in parallel, using the OpenMP standard.
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</description>
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</class>
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<class name="pcl/NormalEstimationTBB" type="NormalEstimationTBB" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimationTBB estimates local surface properties at each 3D point, such as surface normals and curvatures, in
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parallel, using Intel's Threading Building Blocks library.
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</description>
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</class>
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<class name="pcl/NormalEstimation" type="NormalEstimation" base_class_type="nodelet::Nodelet">
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<description>
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NormalEstimation estimates local surface properties at each 3D point, such as surface normals and curvatures.
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</description>
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</class>
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<class name="pcl/PFHEstimation" type="PFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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PFHEstimation estimates the Point Feature Histogram (PFH) descriptor for a given point cloud dataset containing
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points and normals.
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</description>
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</class>
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<class name="pcl/PrincipalCurvaturesEstimation" type="PrincipalCurvaturesEstimation" base_class_type="nodelet::Nodelet">
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<description>
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PrincipalCurvaturesEstimation estimates the directions (eigenvectors) and magnitudes (eigenvalues) of principal surface
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curvatures for a given point cloud dataset containing points and normals.
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</description>
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</class>
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<class name="pcl/VFHEstimation" type="VFHEstimation" base_class_type="nodelet::Nodelet">
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<description>
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VFHEstimation estimates the Viewpoint Feature Histogram (VFH) global descriptor for a given point cloud cluster dataset
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containing points and normals.
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</description>
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</class>
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</library>
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43
pcl_ros/plugins/nodelet/libpcl_ros_filters.xml
Normal file
43
pcl_ros/plugins/nodelet/libpcl_ros_filters.xml
Normal file
@ -0,0 +1,43 @@
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<!-- PCL Filters library component -->
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<library path="lib/libpcl_ros_filters">
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<class name="pcl/PassThrough" type="PassThrough" base_class_type="nodelet::Nodelet">
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<description>
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PassThrough is a filter that uses the basic Filter class mechanisms for passing data around.
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</description>
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</class>
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||||||
|
<class name="pcl/VoxelGrid" type="VoxelGrid" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
VoxelGrid assembles a local 3D grid over a given PointCloud, and uses that to downsample the data.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/ProjectInliers" type="ProjectInliers" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
ProjectInliers uses a model and a set of inlier indices from a PointCloud to project them into a separate PointCloud.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/ExtractIndices" type="ExtractIndices" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
ExtractIndices extracts a set of indices from a PointCloud as a separate PointCloud.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/StatisticalOutlierRemoval" type="StatisticalOutlierRemoval" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
StatisticalOutlierRemoval uses point neighborhood statistics to filter outlier data.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
<class name="pcl/RadiusOutlierRemoval" type="RadiusOutlierRemoval" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
RadiusOutlierRemoval uses point neighborhood statistics to filter outlier data.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/CropBox" type="CropBox" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
CropBox is a filter that allows the user to filter all the data inside of a given box.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
</library>
|
||||||
49
pcl_ros/plugins/nodelet/libpcl_ros_io.xml
Normal file
49
pcl_ros/plugins/nodelet/libpcl_ros_io.xml
Normal file
@ -0,0 +1,49 @@
|
|||||||
|
<!-- PCL IO library component -->
|
||||||
|
<library path="lib/libpcl_ros_io">
|
||||||
|
<class name="pcl/NodeletMUX" type="NodeletMUX" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
NodeletMUX represent a mux nodelet for PointCloud topics: it takes N (up
|
||||||
|
to 8) input topics, and publishes all of them on one output topic.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/NodeletDEMUX" type="NodeletDEMUX" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
NodeletDEMUX represent a demux nodelet for PointCloud topics: it
|
||||||
|
publishes 1 input topic to N output topics.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/PCDReader" type="PCDReader" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
PCDReader reads in a PCD (Point Cloud Data) v.5 file from disk and converts it to a PointCloud message.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/BAGReader" type="BAGReader" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
BAGReader reads in sensor_msgs/PointCloud2 messages from BAG files.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/PCDWriter" type="PCDWriter" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
PCDWriter writes a PointCloud message to disk in a PCD (Point Cloud Data) v.5 file format.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/PointCloudConcatenateFieldsSynchronizer" type="PointCloudConcatenateFieldsSynchronizer" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
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.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
<class name="pcl/PointCloudConcatenateDataSynchronizer" type="PointCloudConcatenateDataSynchronizer" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
PointCloudConcatenateDataSynchronizer is a special form of data
|
||||||
|
synchronizer: it listens for a set of input PointCloud messages on
|
||||||
|
different topics, and concatenates them together into a single PointCloud
|
||||||
|
output message.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
</library>
|
||||||
37
pcl_ros/plugins/nodelet/libpcl_ros_segmentation.xml
Normal file
37
pcl_ros/plugins/nodelet/libpcl_ros_segmentation.xml
Normal file
@ -0,0 +1,37 @@
|
|||||||
|
<!-- PCL Segmentation library component -->
|
||||||
|
<library path="lib/libpcl_ros_segmentation">
|
||||||
|
<class name="pcl/ExtractPolygonalPrismData" type="ExtractPolygonalPrismData" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
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.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/EuclideanClusterExtraction" type="EuclideanClusterExtraction" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
EuclideanClusterExtraction represents a segmentation class for cluster extraction in an Euclidean sense.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/SACSegmentationFromNormals" type="SACSegmentationFromNormals" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
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.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/SACSegmentation" type="SACSegmentation" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
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.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
<class name="pcl/SegmentDifferences" type="SegmentDifferences" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
SegmentDifferences obtains the difference between two spatially aligned point clouds and returns the
|
||||||
|
difference between them for a maximum given distance threshold.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
|
||||||
|
</library>
|
||||||
13
pcl_ros/plugins/nodelet/libpcl_ros_surface.xml
Normal file
13
pcl_ros/plugins/nodelet/libpcl_ros_surface.xml
Normal file
@ -0,0 +1,13 @@
|
|||||||
|
<!-- PCL Surface reconstruction library component -->
|
||||||
|
<library path="lib/libpcl_ros_surface">
|
||||||
|
<class name="pcl/MovingLeastSquares" type="MovingLeastSquares" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
MovingLeastSquares is an implementation of the MLS algorithm for data reconstruction through bivariate polynomial fitting.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
<class name="pcl/ConvexHull2D" type="ConvexHull2D" base_class_type="nodelet::Nodelet">
|
||||||
|
<description>
|
||||||
|
ConvexHull2D represents a 2D ConvexHull implementation.
|
||||||
|
</description>
|
||||||
|
</class>
|
||||||
|
</library>
|
||||||
Loading…
x
Reference in New Issue
Block a user