doc/tutorials/content/narf_feature_extraction.rst
.. _narf_feature_extraction:
This tutorial demonstrates how to extract NARF descriptors at NARF keypoint positions from a range image. The executable enables us to load a point cloud from disc (or create it if not given), extract interest points on it and then calculate the descriptors at these positions. It then visualizes these positions, both in an image and a 3D viewer.
First, create a file called, let's say, narf_feature_extraction.cpp in your favorite
editor, and place the following code inside it:
.. literalinclude:: sources/narf_feature_extraction/narf_feature_extraction.cpp :language: cpp :linenos:
In the beginning we do command line parsing, read a point cloud from disc (or create it if not provided), create a range image and extract NARF keypoints from it. All of these steps are already covered in the previous tutorial NARF keypoint extraction.
The interesting part begins here:
.. code-block:: cpp
... std::vector<int> keypoint_indices2; keypoint_indices2.resize(keypoint_indices.size()); for (unsigned int i=0; i<keypoint_indices.size(); ++i) // This step is necessary to get the right vector type keypoint_indices2[i]=keypoint_indices[i]; ...
Here we copy the indices to the vector used as input for the feature.
.. code-block:: cpp
... pcl::NarfDescriptor narf_descriptor(&range_image, &keypoint_indices2); narf_descriptor.getParameters().support_size = support_size; narf_descriptor.getParameters().rotation_invariant = rotation_invariant; pcl::PointCloudpcl::Narf36 narf_descriptors; narf_descriptor.compute(narf_descriptors); std::cout << "Extracted "<<narf_descriptors.size()<<" descriptors for "<<keypoint_indices.size()<< " keypoints.\n"; ...
This code does the actual calculation of the descriptors. It first creates the NarfDescriptor object and gives it the input data (the keypoint indices and the range image). Then two important parameters are set. The support size, which determines the size of the area from which the descriptor is calculated, and if the rotational invariant (rotation around the normal) version of the NARF descriptor should be used. The we create the output pointcloud and do the actual computation. At last, we output the number of keypoints and the number of extracted descriptors. This numbers can differ. For one, it might happen that the calculation of the descriptor fails, because there are not enough points in the range image (resolution too low). Or there might be multiple descriptors in the same place, but for different dominant rotations.
The resulting PointCloud contains the type Narf36 (see common/include/pcl/point_types.h) and store the descriptor as a 36 elements float and x,y,z,roll,pitch,yaw to describe the local coordinate frame at which the feature was extracted. The descriptors can now be compared, e.g., with the Manhattan distance (sum of absolute differences).
The remaining code just visualizes the keypoint positions in a range image widget and also in a 3D viewer.
Add the following lines to your CMakeLists.txt file:
.. literalinclude:: sources/narf_feature_extraction/CMakeLists.txt :language: cmake :linenos:
After you have made the executable, you can run it. Simply do::
$ ./narf_feature_extraction -m
This will use an autogenerated point cloud of a rectangle floating in space. The key points are detected in the corners. The parameter -m is necessary, since the area around the rectangle is unseen and therefore the system can not detect it as a border. The option -m changes the unseen area to maximum range readings, thereby enabling the system to use these borders.
You can also try it with a point cloud file from your hard drive::
$ ./narf_feature_extraction <point_cloud.pcd>
The output should look similar to this:
.. image:: images/narf_keypoint_extraction.png