  A pressing question when designing intelligent autonomous systems is how to
integrate the various subsystems concerned with complementary tasks. More
specifically, robotic vision must provide task-relevant information about the
environment and the objects in it to various planning related modules. In most
implementations of the traditional Perception-Cognition-Action paradigm these
tasks are treated as quasi-independent modules that function as black boxes for
each other. It is our view that perception can benefit tremendously from a
tight collaboration with cognition. We present RoboSherlock, a
knowledge-enabled cognitive perception systems for mobile robots performing
human-scale everyday manipulation tasks. In RoboSherlock, perception and
interpretation of realistic scenes is formulated as an unstructured information
management(UIM) problem. The application of the UIM principle supports the
implementation of perception systems that can answer task-relevant queries
about objects in a scene, boost object recognition performance by combining the
strengths of multiple perception algorithms, support knowledge-enabled
reasoning about objects and enable automatic and knowledge-driven generation of
processing pipelines. We demonstrate the potential of the proposed framework
through feasibility studies of systems for real-world scene perception that
have been built on top of the framework.
