There are several commands to start recognition using a trained model.

Starting roscore:

    $ roscore

Starting the ROS driver for Kinect:

    $ roslaunch openni_launch openni.launch

Setting the ROS parameters for the Kinect driver:

    $ rosrun dynamic_reconfigure dynparam set /camera/driver depth_registration True
    $ rosrun dynamic_reconfigure dynparam set /camera/driver image_mode 2
    $ rosrun dynamic_reconfigure dynparam set /camera/driver depth_mode 2

Republishing the depth and RGB image topics using topic_tools relay:

    $ rosrun topic_tools relay /camera/depth_registered/image_raw /camera/depth/image_raw
    $ rosrun topic_tools relay /camera/rgb/image_rect_color /camera/rgb/image_raw

Here is the command to start recognition; we can use different pipelines to perform detection. The following command uses the tod pipeline. This will work well for textured objects.

    $ rosrun object_recognition_core detection -c `rospack find object_recognition_tod`/conf/detection.ros.ork --visualize

Alternatively, we can use the tabletop pipeline, which can detect objects placed on a flat surface, such as a table itself:

    $ rosrun object_recognition_core detection -c  `rospack find object_recognition_tabletop`/conf/detection.object.ros.ork

You could also use the linemod pipeline, which is the best for rigid object recognition:

    $ rosrun object_recognition_core detection -c  `rospack find object_recognition_linemod`/conf/detection.object.ros.ork

After running the detectors, we can visualize the detections in Rviz. Let's start Rviz and load the proper display type, shown in the screenshot:

    $ rosrun rviz rviz

The Fixed Frame can be set to camera_rgb_frame. Then, we have to add a PointCloud2 display with the /camera/depth_registered/points topic. To detect the object and display its name, you have to add a new display type called OrkObject, which is installed along with the object-recognition package. You can see the object being detected, as shown in the previous screenshot.

If it is a tabletop pipeline, it will mark the plane area in which object is placed, as shown in the next screenshot. This pipeline is good for grasping objects from a table, which can work well with the ROS MoveIt! package.

For visualizing, you need to add OrkTable with the /table_array topic and MarkerArray with the /tabletop/clusters topic.

We can add any number of objects to the database; detection accuracy depends on the quality of model, quality of 3D input, and processing power of the PC.