Abstract
A bin picking task involves multiple steps, including object detection, 3D pose estimation, and path planning. Dealing with unstructured and cluttered bins presents challenges due to potential overlaps and misidentification of parts. We propose a purely mathematical approach for achieving highly precise 3D pose estimation at the millimeter level. Our method involves capturing a point-cloud image of the parts within the bin and applying a best fit algorithm to smooth the surface. Subsequently, we employ the Iterative Closest Point Method (ICP) to match a CAD model of the desired part with the smoothed surface point-cloud. Finally, we identify gripping points to enable the robot to pick up the part. The concept is validated in a use-case involving Lego sorting. It is compared with an industrial tool for performance evaluation purposes.
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