Deep Workpiece Region Segmentation for Bin Picking
This addresses a specific challenge in industrial automation for bin picking systems, offering an incremental improvement over existing methods.
The paper tackles the problem of distinguishing flat workpieces from bin bottoms in point clouds for industrial bin picking, which causes localization errors, by proposing a real-time segmentation framework using a fully convolutional neural network trained on simulated and real data. The result includes improved detection rates, correct pose estimation, and a reduction in computation time by approximately 1 second.
For most industrial bin picking solutions, the pose of a workpiece is localized by matching a CAD model to point cloud obtained from 3D sensor. Distinguishing flat workpieces from bottom of the bin in point cloud imposes challenges in the localization of workpieces that lead to wrong or phantom detections. In this paper, we propose a framework that solves this problem by automatically segmenting workpiece regions from non-workpiece regions in a point cloud data. It is done in real time by applying a fully convolutional neural network trained on both simulated and real data. The real data has been labelled by our novel technique which automatically generates ground truth labels for real point clouds. Along with real time workpiece segmentation, our framework also helps in improving the number of detected workpieces and estimating the correct object poses. Moreover, it decreases the computation time by approximately 1s due to a reduction of the search space for the object pose estimation.