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Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 31181


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10011779
Deep Learning Based 6D Pose Estimation for Bin-Picking Using 3D Point Clouds
Abstract:
Estimating the 6D pose of objects is a core step for robot bin-picking tasks. The problem is that various objects are usually randomly stacked with heavy occlusion in real applications. In this work, we propose a method to regress 6D poses by predicting three points for each object in the 3D point cloud through deep learning. To solve the ambiguity of symmetric pose, we propose a labeling method to help the network converge better. Based on the predicted pose, an iterative method is employed for pose optimization. In real-world experiments, our method outperforms the classical approach in both precision and recall.
Digital Object Identifier (DOI):

References:

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