Detection and Pose Determination of a Part for Bin Picking Bc

This thesis discusses the visual bin picking task, which is the task of sequential unloading a bin one part at a time using a camera as a primary source of information. The semi-structured variant of the bin picking task is considered. In this thesis, a solution for this problem that is based on learning the appearance model of a part using convolutional neural networks is proposed. Thus, no hard-coded geometry of a part is required. The models in the developed system predict the poses of the parts and detect occlusions. The proposed system has been implemented and tested with a metallic strut bracket. The experiments have shown that the achieved estimated success rate of the system is 95 % of acquiring attempts.

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