3. Point Cloud Library (PCL)¶
Note
References: Ros Industrial training. PCL_Tools.
3.1. PCL tools¶
Publish point cloud on the topic topic_cloud_pcd:
roscore
rosrun pcl_ros pcd_to_pointcloud table.pcd 0.1 _frame_id:=map cloud_pcd:=topic_cloud_pcd
rosrun rviz rviz
In order to see the point cloud data, add PointCloud2 display and select the topic topic_cloud_pcd.
Install pcl-tools
sudo apt install pcl-tools
Point cloud data can be viewed using the command line pcl_viewer, this command is not part of ROS, so no need to run roscore.
pcl_viewer table.pcd
3.1.1. Downsample the point cloud using the pcl_voxel_grid¶
Downsample the original point cloud using a voxel grid with a grid size of (0.05,0.05,0.05). In a voxel grid, all points in a single grid cube are replaced with a single point at the center of the voxel. This is a common method to simplify overly complex/detailed sensor data, to speed up processing steps.
pcl_voxel_grid table.pcd table_downsampled.pcd -leaf 0.05,0.05,0.05
pcl_viewer table_downsampled.pcd
3.1.2. Segmentation pcl_sac_segmentation_plane¶
Extract the table surface, find the largest plane and extract points that belong to that plane (within a given threshold).
pcl_sac_segmentation_plane table_downsampled.pcd only_table.pcd -thresh 0.01
pcl_viewer only_table.pcd
Extract the largest point-cluster not belonging to the table.
pcl_sac_segmentation_plane table.pcd object_on_table.pcd -thresh 0.01 -neg 1
pcl_viewer object_on_table.pcd
3.1.3. Remove outliers pcl_outlier_removal¶
For this example, a statistical method will be used for removing outliers. This is useful to clean up noisy sensor data, removing false artifacts before further processing.
pcl_outlier_removal table.pcd table_outlier_removal.pcd -method statistical
pcl_viewer table_outlier_removal.pcd
3.1.4. Compute the normals pcl_normal_estimation¶
This example estimates the local surface normal (perpendicular) vectors at each point. For each point, the algorithm uses nearby points (within the specified radius) to fit a plane and calculate the normal vector. Zoom in to view the normal vectors in more detail.
pcl_normal_estimation only_table.pcd table_normals.pcd -radius 0.1
pcl_viewer table_normals.pcd -normals 10
3.1.5. Mesh a point cloud pcl_marching_cubes_reconstruction¶
Point cloud data is often unstructured, but sometimes processing algorithms need to operate on a more structured surface mesh. This example uses the “marching cubes” algorithm to construct a surface mesh that approximates the point cloud data.
pcl_marching_cubes_reconstruction table_normals.pcd table_mesh.vtk -grid_res 20
pcl_viewer table_mesh.vtk