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BLORT - The Blocks World Robotic Vision Toolbox

The vision and robotics communities have developed a large number of increasingly successful methods for tracking, recognizing and online learning of objects, all of which have their particular strengths and weaknesses. A researcher aiming to provide a robot with the ability to handle objects will typically have to pick amongst these and engineer a system that works for her particular setting. The toolbox is aimed at robotics research and as such we have in mind objects typically of interest for robotic manipulation scenarios, e.g. mugs, boxes and packaging of various sorts. We are not aiming to cover articulated objects (such as walking humans), highly irregular objects (such as potted plants) or deformable objects (such as cables). The system does not require specialized hardware and simply uses a single camera allowing usage on about any robot. The toolbox integrates state-of-the art methods for detection and learning of novel objects, and recognition and tracking of learned models.


The system works with a CAD model of the object provided. The current implementation of the BLORT detector module uses SIFT feature descriptors to provide an approximate estimation of the object's pose for the tracker module which will track the object using edge-based methods.

More information: [Mörwald, T.; Prankl, J.; Richtsfeld, A.; Zillich, M.; Vincze, M. BLORT - The Blocks World Robotic Vision Toolbox Best Practice in 3D Perception and Modeling for Mobile Manipulation (in conjunction with ICRA 2010), 2010.] or you can also visit the BLORT Homepage.

Wiki: blort (last edited 2012-07-03 16:08:04 by BenceMagyar)