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One of the remaining obstacles to a widespread introduction of industrial robots is their inability to deal with 3D objects in a bin that are not precisely positioned, i.e., the bin-picking problem. In this work we address the general bin-picking problem where a CAD model of the object to be picked is available beforehand. Structured light, in the form of Time Multiplexed Binary Stripes, is used together with a calibrated camera to obtain 3D data of the objects in the bin. The 3D data is then segmented into points of interest and for each a regional feature vector is extracted. The features are the Harmonic Shape Contexts. These are characterized by being rotational invariant and can in general model any free-form object. The Harmonic Shape Contexts are extracted from the 3D scene data and matched against similar features found in the CAD model. This allows for a pose estimation of the objects in the bin. Tests show the method to be capable of pose estimating partial-occluded objects, however, the method is also found to be sensitive to the resolution in the structured light system and to noise in the data. [ABSTRACT FROM AUTHOR]

Increased reliance on optimistic data replication has led to burgeoning interest in tools and frameworks for synchronizing disconnected updates to replicated data. We have implemented a generic synchronization framework, called Harmony, that can be used to build statebased synchronizers for a wide variety of tree-structured data formats. A novel feature of this framework is that the synchronization process—in particular, the recognition of conflicts—is driven by the schema of the structures being synchronized. We formalize Harmony's synchronization algorithm, state a simple and intuitive specification, and illustrate how it can be used to synchronize trees representing a variety of specific forms of application data, including sets, records, and tuples. [ABSTRACT FROM AUTHOR]