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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:

More information about PCL integration in ROS (e.g. point cloud data types, publishing/subscribing) can be found in the ROS/PCL overview.

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

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:

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:

   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);

   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 }

Note

Since different tutorials will often use different variable names for their inputs and outputs, remember that you may need to modify the code slightly when integrating the tutorial code into your own ROS node. In this case, notice that we had to change the variable name input to cloud, and output to cloud_filtered in order to match up with the code from the tutorial we copied.

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

If you'd like to save yourself some copying and pasting, you can download the source file for this example here.

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:

   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);

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 }

  • Notice that we added a conversion step from sensor_msgs/PointCloud2 to pcl/PointCloud<T> on the first two lines, and we also changed the variable that we publish from output to coefficients.

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

As with the previous example, if you want to skip a few steps, you can download the source file for this example here.


2020-08-08 12:30