Cylinder model segmentation

.. _cylinder_segmentation:

This tutorial exemplifies how to run a Sample Consensus segmentation for cylindrical models. To make the example a bit more practical, the following operations are applied to the input dataset (in order):

data points further away than 1.5 meters are filtered
surface normals at each point are estimated
a plane model (describing the table in our demo dataset) is segmented and saved to disk
a cylindrical model (describing the mug in our demo dataset) is segmented and saved to disk

.. raw:: html

.. note:: The cylindrical model is not perfect due to the presence of noise in the data.

The code

First, download the dataset table_scene_mug_stereo_textured.pcd <https://raw.github.com/PointCloudLibrary/data/master/tutorials/table_scene_mug_stereo_textured.pcd>_ and save it somewhere to disk.

Then, create a file, let's say, cylinder_segmentation.cpp in your favorite editor, and place the following inside it:

.. literalinclude:: sources/cylinder_segmentation/cylinder_segmentation.cpp :language: cpp :linenos:

The explanation

The only relevant lines are the lines below, as the other operations are already described in the other tutorials.

.. literalinclude:: sources/cylinder_segmentation/cylinder_segmentation.cpp :language: cpp :lines: 83-91

As seen, we're using a RANSAC robust estimator to obtain the cylinder coefficients, and we're imposing a distance threshold from each inlier point to the model no greater than 5cm. In addition, we set the surface normals influence to a weight of 0.1, and we limit the radius of the cylindrical model to be smaller than 10cm.

Compiling and running the program

Add the following lines to your CMakeLists.txt file:

.. literalinclude:: sources/cylinder_segmentation/CMakeLists.txt :language: cmake :linenos:

After you have made the executable, you can run it. Simply do::

$ ./cylinder_segmentation

You will see something similar to::

PointCloud has: 307200 data points. PointCloud after filtering has: 139897 data points. [pcl::SACSegmentationFromNormals::initSACModel] Using a model of type: SACMODEL_NORMAL_PLANE [pcl::SACSegmentationFromNormals::initSACModel] Setting normal distance weight to 0.100000 [pcl::SACSegmentationFromNormals::initSAC] Using a method of type: SAC_RANSAC with a model threshold of 0.030000 [pcl::SACSegmentationFromNormals::initSAC] Setting the maximum number of iterations to 100 Plane coefficients: header: seq: 0 stamp: 0.000000000 frame_id: values[] values[0]: -0.0161854 values[1]: 0.837724 values[2]: 0.545855 values[3]: -0.528787

PointCloud representing the planar component: 117410 data points. [pcl::SACSegmentationFromNormals::initSACModel] Using a model of type: SACMODEL_CYLINDER [pcl::SACSegmentationFromNormals::initSACModel] Setting radius limits to 0.000000/0.100000 [pcl::SACSegmentationFromNormals::initSACModel] Setting normal distance weight to 0.100000 [pcl::SACSegmentationFromNormals::initSAC] Using a method of type: SAC_RANSAC with a model threshold of 0.050000 [pcl::SampleConsensusModelCylinder::optimizeModelCoefficients] LM solver finished with exit code 2, having a residual norm of 0.322616. Initial solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0422902 Final solution: 0.0452105 0.0924601 0.790215 0.20495 -0.721649 -0.661225 0.0396354 Cylinder coefficients: header: seq: 0 stamp: 0.000000000 frame_id: values[] values[0]: 0.0452105 values[1]: 0.0924601 values[2]: 0.790215 values[3]: 0.20495 values[4]: -0.721649 values[5]: -0.661225 values[6]: 0.0396354

PointCloud representing the cylindrical component: 8625 data points.