Pose Estimation Using Structured Light and Harmonic Shape Contexts
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.
KeywordsBin-picking rotational invariant features surface mesh time multiplexed binary stripes CAD model
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