A comprehensive list of PCL tutorials can be found on PCL's external website. Here are a few of the tutorials that you might want to check out:

• Reading point cloud data from PCD files

• Downsampling a PointCloud using a VoxelGrid filter

• Planar model segmentation

• Spatial change detection on unorganized point cloud data

• Smoothing and normal estimation based on polynomial reconstruction

• Fast triangulation of unordered point clouds

• Aligning object templates to a point cloud

The following tutorial describes how to use any of the existing tutorials on http://pointclouds.org in a ROS ecosystem using nodes or nodelets.

How to use a PCL tutorial in ROS

catkin rosbuild

Create a ROS package

roscreate-pkg my_pcl_tutorial pcl pcl_ros roscpp sensor_msgs

catkin_create_pkg my_pcl_tutorial pcl pcl_ros roscpp sensor_msgs

for hydro use

catkin_create_pkg my_pcl_tutorial pcl_conversions pcl_ros roscpp sensor_msgs 

This will create a new ROS package with the necessary dependencies.

For Hydro modify the package.xml to add

  <build_depend>libpcl-all-dev</build_depend>
  <run_depend>libpcl-all</run_depend>

Create the code skeleton

Create an empty file called src/example.cpp and paste the following code in it:

   1 #include <ros/ros.h>
   2 #include <sensor_msgs/PointCloud2.h>
   3 // PCL specific includes
   4 #include <pcl/ros/conversions.h>
   5 #include <pcl/point_cloud.h>
   6 #include <pcl/point_types.h>
   7 
   8 ros::Publisher pub;
   9 
  10 void 
  11 cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
  12 {
  13   // ... do data processing
  14 
  15   sensor_msgs::PointCloud2 output;
  16   // Publish the data
  17   pub.publish (output);
  18 }
  19 
  20 int
  21 main (int argc, char** argv)
  22 {
  23   // Initialize ROS
  24   ros::init (argc, argv, "my_pcl_tutorial");
  25   ros::NodeHandle nh;
  26 
  27   // Create a ROS subscriber for the input point cloud
  28   ros::Subscriber sub = nh.subscribe ("input", 1, cloud_cb);
  29 
  30   // Create a ROS publisher for the output point cloud
  31   pub = nh.advertise<sensor_msgs::PointCloud2> ("output", 1);
  32 
  33   // Spin
  34   ros::spin ();
  35 }

The code above does nothing but initialize ROS, create a subscriber and a publisher for PointCloud2 data.

Add the source file to CMakeLists.txt

Edit the CMakeLists.txt file in your newly created package and add:

rosbuild_add_executable (example src/example.cpp)

add_executable(example src/example.cpp)
target_link_libraries(example ${catkin_LIBRARIES})

Download the source code from the PCL tutorial

All PCL versions prior to 2.0, have two different ways of representing point cloud data:

• through a sensor_msgs/PointCloud2 ROS message

• through a pcl/PointCloud<T> data structure

The following two code examples will discuss both formats.

sensor_msgs/PointCloud2

The sensor_msgs/PointCloud2 format was designed as a ROS message, and is the preferred choice for ROS applications. In the following example, we downsample a PointCloud2 structure using a 3D grid, thus reducing the number of points in the input dataset considerably.

To add this capability to the code skeleton above, perform the following steps:

• visit http://www.pointclouds.org/documentation/, click on Tutorials, then navigate to the Downsampling a PointCloud using a VoxelGrid filter tutorial (http://www.pointclouds.org/documentation/tutorials/voxel_grid.php)

• read the code and the explanations provided there. You will notice that the code breaks down essentially in 3 parts:
  • load the cloud (lines 9-19)
  • process the cloud (lines 20-24)
  • save the output (lines 25-32)
• since we use ROS subscribers and publishers in our code snippet above, we can ignore the loading and saving of point cloud data using the PCD format. Thus, the only relevant part in the tutorial remains lines 20-24 that create the PCL object, pass the input data, and perform the actual computation:

   1   // Create the filtering object
   2   pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
   3   sor.setInputCloud (cloud);
   4   sor.setLeafSize (0.01, 0.01, 0.01);
   5   sor.filter (*cloud_filtered);

• In these lines, the input dataset is named cloud, and the output dataset is called cloud_filtered. Copy these lines, in the code snippet above, by modifying the callback function as follows:

   1 #include <pcl/filters/voxel_grid.h>
   2 
   3 ...
   4 
   5 void 
   6 cloud_cb (const sensor_msgs::PointCloud2ConstPtr& cloud)
   7 {
   8   sensor_msgs::PointCloud2 cloud_filtered;
   9 
  10   // Perform the actual filtering
  11   pcl::VoxelGrid<sensor_msgs::PointCloud2> sor;
  12   sor.setInputCloud (cloud);
  13   sor.setLeafSize (0.01, 0.01, 0.01);
  14   sor.filter (cloud_filtered);
  15 
  16   // Publish the data
  17   pub.publish (cloud_filtered);
  18 }

Save the output file then build:

$ rosmake my_pcl_tutorial

$ cd %TOP_DIR_YOUR_CATKIN_HOME%
$ catkin_make

Then run:

rosrun my_pcl_tutorial example input:=/narrow_stereo_textured/points2

For Hydro, make following changes in the code

Delete:

#include <pcl/ros/conversions.h>

Add:

 #include <pcl_conversions/pcl_conversions.h>
 #include <sensor_msgs/PointCloud2.h>

Modified the callback function to use pcl::PCLPointCloud2 instead of sensor_msgs::PointCloud2 as:-<br> (It's given at hydro migration : http://wiki.ros.org/hydro/Migration)

void 
cloud_cb (const pcl::PCLPointCloud2ConstPtr& input)
{
  pcl::PCLPointCloud2 cloud_filtered;
  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;
  sor.setInputCloud (input);
  sor.setLeafSize (0.01, 0.01, 0.01);
  sor.filter (cloud_filtered);

  // Publish the dataSize 
  pub.publish (cloud_filtered);
}

pcl/PointCloud<T>

The pcl/PointCloud<T> format represents the internal PCL point cloud format. For modularity and efficiency reasons, the format is templated on the point type, and PCL provides a list of templated common types which are SSE aligned. In the following example, we estimate the planar coefficients of the largest plane found in a scene.

To add this capability to the code skeleton above, perform the following steps:

• visit http://www.pointclouds.org/documentation/, click on Tutorials, then navigate to the Planar model segmentation tutorial (http://www.pointclouds.org/documentation/tutorials/planar_segmentation.php)

• read the code and the explanations provided there. You will notice that the code breaks down essentially in 3 parts:
  • create a cloud and populate it with values (lines 12-30)
  • process the cloud (38-56)
  • write the coefficients (58-68)
• since we use ROS subscribers in our code snippet above, we can ignore the first step, and just process the cloud received on the callback directly. Thus, the only relevant part in the tutorial remains lines 38-56 that create the PCL object, pass the input data, and perform the actual computation:

   1   pcl::ModelCoefficients coefficients;
   2   pcl::PointIndices inliers;
   3   // Create the segmentation object
   4   pcl::SACSegmentation<pcl::PointXYZ> seg;
   5   // Optional
   6   seg.setOptimizeCoefficients (true);
   7   // Mandatory
   8   seg.setModelType (pcl::SACMODEL_PLANE);
   9   seg.setMethodType (pcl::SAC_RANSAC);
  10   seg.setDistanceThreshold (0.01);
  11 
  12   seg.setInputCloud (cloud.makeShared ());
  13   seg.segment (inliers, coefficients);

• In these lines, the input dataset is named cloud and is of type pcl::PointCloud<pcl::PointXYZ>, and the output is represented by a set of point indices that contain the plane together with the plane coefficients. cloud.makeShared() creates a boost shared_ptr object for the object cloud (see the pcl::PointCloud API documentation).

Copy these lines, in the code snippet above, by modifying the callback function as follows:

   1 #include <pcl/sample_consensus/model_types.h>
   2 #include <pcl/sample_consensus/method_types.h>
   3 #include <pcl/segmentation/sac_segmentation.h>
   4 
   5 ...
   6 
   7 void 
   8 cloud_cb (const sensor_msgs::PointCloud2ConstPtr& input)
   9 {
  10   // Convert the sensor_msgs/PointCloud2 data to pcl/PointCloud
  11   pcl::PointCloud<pcl::PointXYZ> cloud;
  12   pcl::fromROSMsg (*input, cloud);
  13 
  14   pcl::ModelCoefficients coefficients;
  15   pcl::PointIndices inliers;
  16   // Create the segmentation object
  17   pcl::SACSegmentation<pcl::PointXYZ> seg;
  18   // Optional
  19   seg.setOptimizeCoefficients (true);
  20   // Mandatory
  21   seg.setModelType (pcl::SACMODEL_PLANE);
  22   seg.setMethodType (pcl::SAC_RANSAC);
  23   seg.setDistanceThreshold (0.01);
  24 
  25   seg.setInputCloud (cloud.makeShared ());
  26   seg.segment (inliers, coefficients);
  27 
  28   // Publish the model coefficients
  29   pub.publish (coefficients);
  30 }

In addition, since we're now publishing the planar model coefficients found rather than point cloud data, we have to change our publisher type from:

  // Create a ROS publisher for the output point cloud
  pub = nh.advertise<sensor_msgs::PointCloud2> ("output", 1);

to:

  // Create a ROS publisher for the output model coefficients
  pub = nh.advertise<pcl::ModelCoefficients> ("output", 1);

Save the output file, then compile and run the code above:

rosrun my_pcl_tutorial example input:=/narrow_stereo_textured/points2