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write_an_interface_to_your_perception_system [2013/01/07 10:26] – [KnowRob integration] tenorthwrite_an_interface_to_your_perception_system [2013/02/12 17:41] – external edit 127.0.0.1
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 </code> </code>
  
 +Such a manual query requires that the user is aware of the existence of this service. It also requires adaptation of the query whenever the context changes, e.g. when different or multiple recognition systems are used. We can avoid these problems by wrapping the predicate into a computable; with this definition, the predicate will automatically be called whenever the user asks for an object pose and when the service interface is available. The following OWL code defines a computable Prolog class for the example predicate:
  
 <code> <code>
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 </computable:PrologClass>   </computable:PrologClass>  
 </code> </code>
 +
 +Instead of calling the service directly, we can now query for object poses and obtain -- in addition to already known poses from e.g. a semantic map -- the poses generated by our service:
 +<code>
 +?- rdfs_instance_of(A, knowrob:'HumanScaleObject').
 +A = 'http://ias.cs.tum.edu/kb/knowrob.owl#TableKnife_vUXiHMJy'.
 +</code>
 +
 +
 +
 +====== Other kinds of perception systems ======
 +
 +In this tutorial, we have concentrated on object recognition as a special case of perception. 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.
  
  
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 ====== Adapting the examples to your system ====== ====== Adapting the examples to your system ======
  
 +To keep the examples as simple and self-contained as possible, we have defined our own dummy components and messages. Your perception system will probably use slightly different messages and may provide more or less information. In this case, you will need to adapt the service client or topic listener to correctly extract information from your messages. After creating the object instance with ''create_object_perception'', you can use ''rdf_assert'' to add further properties to the object (e.g. color, weight, etc).
  
  
-====== 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.