RoboSherlock: Unstructured information processing for robot perception

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.

Authors

• Michael Beetz
• Ferenc Balint-Benczedi
• Nico Blodow
• Daniel Nyga
• Thiemo Wiedemeyer
• Zoltán-Csaba Márton

Keywords

• Robot sensing systems
• Cognition
• Shape
• Engines
• Containers
• Unstructured Data
• Perception Of The Robot
• Manipulation Tasks
• Perceptual System
• Objects In The Scene
• Open Framework
• Data Structure
• Knowledge Base
• Mobile App
• Web Server
• Metadata
• Sensor Data
• Point Cloud
• Daily Use
• Hypothesis Generation
• Human Environment
• Perceptual Task
• Electrical Devices
• Orange Juice
• 3D Pose
• Robotic Agents
• Object Pose
• Region Of Interest Image
• Object Volume
• Ketchup