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- W2956571887 abstract "Quick and accurate estimation for the 6-DoF pose of a randomly arranged object in intricate shape plays an important role in robotic picking applications. In this paper we propose an approach based on template matching by using the aligned RGB-D image with prior knowledge to recover the 6-DoF pose of a randomly arranged object. First, the object’s template database is generated with the help of a defined virtual imaging model and its CAD model. Then in the practical phase, we segment RGB-D image to get the mask representing the location of the object and then these data are modified into a comparable format with the characteristics of scale invariance. At last, a similar function with adjustable attention weight to color and depth data is defined to find Top-K matched templates. The selected matched templates are refined by ICP to generate the final answer. Experiments are conducted using an RGB-D camera and a robot arm to pick up given objects in intricate shape. The average recognition rate of the object in different poses is 97.826%. It also can work well with multiple objects randomly arranged with good masks representing the locations." @default.
- W2956571887 created "2019-07-23" @default.
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- W2956571887 date "2019-05-01" @default.
- W2956571887 modified "2023-09-27" @default.
- W2956571887 title "6DoF Pose Estimation for Intricately-Shaped Object with Prior Knowledge for Robotic Picking" @default.
- W2956571887 doi "https://doi.org/10.1109/isass.2019.8757758" @default.
- W2956571887 hasPublicationYear "2019" @default.
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