RoboSherlock: unstructured information processing for robot perception

Abstract

We present ROBOSHERLOCK, an open source software framework for implementing perception systems for 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 by three feasibility studies of systems for real-world scene perception that have been built on top of ROBOSHERLOCK.