This package allows for the tracking of moving (rotating) objects and the estimation of the relevant motion parameters. In the case of the rotation the estimated parameters are the center of rotation, the rotation axis and the rotation speed.
How it works
object_tracker package uses the the output provided by the ROS
Object Recognition Kitchen. This package examines subsequent detection
results looking for moving objects and, if found, is able to determine
the relevant rotation parameters, i.e. the center and axis of rotation
and the angular speed. Some statistical indicators (covariances) are also
output to give an idea of the stability in time and spatial location of
the estimated parameters.
There is only one input needed, in the form of a topic of type
The topic name is by default
This package has various outputs:
- TF data: the center of rotation determines a moving (rotating) reference frame, each tracked object in addition is associated to a static reference frame whose coordinates are expressed in relation to the rotating frame expressed by the center of rotation.
rotating_objects_markers: topic of type
visualization_msgs/MarkerArray, contains a spherical marker for each of the objects currently being tracked.
rotating_objects: topic of type
object_tracker/RotatingObjects; the message contains the current rotation parameters, the covariances and a list of the tracked objects including their respective radii and phases.
recognized_rotating_objects: topic of type
object_recognition_msgs/RecognizedObjectArray, contains the same data as the input
recognized_object_arraybut with the object poses expressed using the rotating reference frame instead of the original TF frame.
This package depends on the Object Recognition Kitchen hence you need to have in your ROS setup the following packages:
You also need an object recognition algorithm; while you can use any
algorithm of your choice the suggested one is
Inside your Catkin workspace clone this repository:
$ git clone https://github.com/wg-perception/object_tracker.git
Then simply run
to generate the message, service and dynamic_reconfigure files.
To launch the tracker with the default parameters and having it launch for you the tabletop detector simply run:
$ roslaunch object_tracker track.launch
Alternatively, if you prefer launching a different object detector or you want the maximum flexibility regarding topic names you can do this:
$ rosrun object_recognition_ros server.py -c /path/to/recognition/config/file $ rosrun object_tracker estimate_rotation_server.py $ rosrun object_tracker multi_object_tracker.py
There are various parameters that can be set to fine tune the rotation model estimation; the default values should be good for a variety of practical cases but, if you want to change the behavior, you can do so by running:
$ rosrun dynamic_reconfigure reconfigure_gui /objects_tracker
To understand the meaning of each parameter simply hover your mouse on top of its name or have a look inside the [cfg/RotatingObjectTracker.cfg] file.