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Write an interface to your perception system
There are two main approaches how perception can be performed: Some perception algorithms continuously detect objects and output the results (in ROS terminology: publish the results on a topic), others perform recognition only on demand (in ROS: by calling a service). These two kinds of systems need to be interfaced in different ways: The former requires a topic listener that records the published object detections and adds them to the knowledge base, the latter can be interfaced by computables that trigger the perception procedure when a query involves the respective information.
In this tutorial, we explain on two minimal examples how to write interfaces to these two kinds of perception systems. Currently, there is no 'standard' perception system in ROS, so some manual work is still needed to interface your favourite object recognition with KnowRob. We therefore created two 'dummy' perception systems that output simulated random object detections. It should however be very easy to adapt the examples to any real perception system.
Before starting with the tutorial, it is important to first understand how object detections are represented in KnowRob. Further information on this topic can be found in Sections 3.2 and 6.1 in http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:91-diss-20111125-1079930-1-7.
Setting up the perception tutorial
The knowrob_perception tutorial is part of the knowrob_tutorials repository. You need to check it out into your ROS workspace (i.e. into a directory that is part of your ROS_PACKAGE_PATH). https://github.com/knowrob/knowrob_tutorials.git
git clone https://github.com:knowrob/knowrob_tutorials.git
After the checkout, you should be able to roscd
into the knowrob_perception directory and to rosmake
the package. If the 'roscd' command does not change your working directory to the knowrob_perception directory, please check your ROS configuration (e.g. make sure knowrob_perception is part of the ROS_PACKAGE_PATH), because otherwise the following steps will fail as well. Both commands should work without any error messages.
roscd knowrob_perception rosmake
Interfacing topic-based perception systems
Publisher
The file src/edu/tum/cs/ias/knowrob/tutorial/DummyPublisher.java implements a simple dummy publisher that simulates a perception system that regularly detects objects and publishes these detections on the /dummy_object_detections topic once a second. Try to understand how these detections are generated by the generateDummyObjectDetection() method. You can start the publisher using
rosrun knowrob_perception_tutorial dummy_publisher
Once the publisher is running, you can have a look at the generated object poses by calling the following command from a different terminal.
rostopic echo /dummy_object_detections
It should output messages of the following form:
type: DinnerFork pose: header: seq: 0 stamp: secs: 1357547989 nsecs: 196672575 frame_id: map pose: position: x: 0.300724629488 y: 2.96134330258 z: 1.56672560148 orientation: x: 0.0 y: 0.0 z: 0.0 w: 1.0
Subscriber
The counterpart on the client side that consumes the object detections is implemented in the file src/edu/tum/cs/ias/knowrob/tutorial/DummySubscriber.java
. The subscriber has been realized using two threads: The listenToObjDetections
thread subscribes to the topic and puts the incoming messages into the callback
queue. The updateKnowRobObjDetections
thread processes all object detections in this queue and creates the corresponding representations in KnowRob. The rationale behind this setup is to keep the subscriber thread as light-weight as possible to avoid problems of missing messages when the program is occupied updating the knowledge base. While the creation of an object detection in KnowRob is quite fast, this structure is crucial if the processing becomes more complex.
The following code snippet is the main part of the updateKnowRobObjDetections
thread. It pops the an ObjectDetection
message from the callback
queue, converts the pose quaternion into a 4×4 pose matrix and calls the create_object_perception
predicate.
while (n.isValid()) { obj = callback.pop(); Matrix4d p = quaternionToMatrix(obj.pose.pose); String q = "create_object_perception(" + "'http://ias.cs.tum.edu/kb/knowrob.owl#"+obj.type+"', [" + p.m00 + ","+ p.m01 + ","+ p.m02 + ","+ p.m03 + "," + p.m10 + ","+ p.m11 + ","+ p.m12 + ","+ p.m13 + "," + p.m20 + ","+ p.m21 + ","+ p.m22 + ","+ p.m23 + "," + p.m30 + ","+ p.m31 + ","+ p.m32 + ","+ p.m33 + "], ['DummyObjectDetection'], ObjInst)"; PrologInterface.executeQuery(q); n.spinOnce(); }
This predicate, defined in the knowrob_perception package, is defined as below. It creates a new object instance for the given object type (rdf_instance_from_class
), creates an instance describing the perception event using the PerceptionTypes
given as argument, links the object instance to this perception event, and sets the pose at which the object was detected. This convenience predicate assumes that the detected objects are always novel. By calling the different predicates individually, one can also add perception events to existing objects instances if the identity of the object instance is known.
create_object_perception(ObjClass, ObjPose, PerceptionTypes, ObjInst) :- rdf_instance_from_class(ObjClass, ObjInst), create_perception_instance(PerceptionTypes, Perception), set_object_perception(ObjInst, Perception), set_perception_pose(Perception, ObjPose).
KnowRob integration
Whenever the subscriber is started, it creates the KnowRob-internal representations for all object detections that are received via the topic. It can be run from Prolog via the JPL Java-Prolog interface as defined in prolog/perception_tutorial.pl:
obj_detections_listener(Listener) :- jpl_new('edu.tum.cs.ias.knowrob.tutorial.DummySubscriber', ['knowrob_tutorial_listener'], Listener), jpl_call(Listener, 'startObjDetectionsListener', [], _).
If the dummy publisher is running, the following sequence of commands starts the topic listener, queries for object instances and their poses.
?- obj_detections_listener(L). L = @'J#00000000000173056232'. Attaching 0x8afd1010 <wait for a few seconds...> ?- owl_individual_of(A, knowrob:'HumanScaleObject'). A = 'http://ias.cs.tum.edu/kb/knowrob.owl#Cup_vUXiHMJy' ; A = 'http://ias.cs.tum.edu/kb/knowrob.owl#Cup_bneXbLGX' ; A = 'http://ias.cs.tum.edu/kb/knowrob.owl#DinnerFork_TaVWzXre' ?- current_object_pose('http://ias.cs.tum.edu/kb/knowrob.owl#DinnerFork_TaVWzXre', P). P = [1.0,0.0,0.0,2.9473,0.0,1.0,0.0,2.6113,0.0,0.0,1.0,0.2590,0.0,0.0,0.0,1.0].
Interfacing service-based perception systems
Perception service
The dummy perception service is very similar to the dummy publisher. Whenever a request for an object detection is received, it responds with a simulated detection of a random object type at a random pose. In real scenarios, the request will probably not be empty, but specify properties of the perception method to be used. The code of the dummy service can be found in the file src/edu/tum/cs/ias/knowrob/tutorial/DummyService.java. It can be started with the following command:
rosrun knowrob_perception_tutorial dummy_service
Service client
The callObjDetectionService()
method in the service client simply calls the dummy ROS service and returns the ObjectDetection
message returned by the service call. The code can be found in the file src/edu/tum/cs/ias/knowrob/tutorial/DummyClient.java.
KnowRob integration
implemented in prolog/perception_tutorial.pl
integrated as computable prolog class
in contrast to topic-based example, which performed most processing on the Java side, we are doing more of the processing on the Prolog side
comp_object_detection(_ObjClass, ObjInst) :- % Call the DetectObject service for retrieving a new object detection. % The method returns a reference to the Java ObjectDetection message object jpl_call('edu.tum.cs.ias.knowrob.tutorial.DummyClient', 'callObjDetectionService', [], ObjectDetection), % Read information from the ObjectDetection object % Read type -> simple string; combine with KnowRob namespace jpl_get(ObjectDetection, 'type', T), atom_concat('http://ias.cs.tum.edu/kb/knowrob.owl#', T, Type), % Read pose -> convert from quaternion to pose list jpl_get(ObjectDetection, 'pose', PoseStamped), jpl_get(PoseStamped, 'pose', PoseQuat), jpl_call('edu.tum.cs.ias.knowrob.tutorial.DummyClient', 'quaternionToMatrix', [PoseQuat], PoseMatrix), knowrob_coordinates:matrix4d_to_list(PoseMatrix,PoseList), % Create the object representations in the knowledge base % The third argument is the type of object perception describing % the method how the object has been detected create_object_perception(Type, PoseList, ['DummyObjectDetection'], ObjInst).
Adapting the examples to your system
Other kinds of perception systems
In this tutorial, we have concentrated on object recognition as a special case of a perception task. There are of course other perception tasks like the identification and pose estimation of humans, recognition and interpretation of spoken commands, etc. Most of these systems can however be interfaced in a very similar way: If they produce information continuously and asynchronously, a topic-based interface can be used. If they compute information on demand, the computable-based interface can be adapted.