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IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts.

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Title: IPPE-PCR: a novel 6D pose estimation method based on point cloud repair for texture-less and occluded industrial parts.
Authors: Qin, Wei1 (AUTHOR) wqin@sjtu.edu.cn, Hu, Qing1 (AUTHOR), Zhuang, Zilong1 (AUTHOR) zilongzhuang@163.com, Huang, Haozhe1 (AUTHOR), Zhu, Xiaodan2 (AUTHOR), Han, Lin2 (AUTHOR)
Source: Journal of Intelligent Manufacturing. Aug2023, Vol. 34 Issue 6, p2797-2807. 11p.
Subjects: Point cloud, Repairing, Everyday life
Abstract: Fast and accurate 6D pose estimation can help a robot arm grab industrial parts efficiently. The previous 6D pose estimation algorithms mostly target common items in daily life. Few algorithms are aimed at texture-less and occluded industrial parts and there are few industrial parts datasets. A novel method called the Industrial Parts 6D Pose Estimation framework based on point cloud repair (IPPE-PCR) is proposed in this paper. A synthetic dataset of industrial parts (SD-IP) is established as the training set for IPPE-PCR and an annotated real-world, low-texture and occluded dataset of industrial parts (LTO-IP) is constructed as the test set for IPPE. To improve the estimation accuracy, a new loss function is used for the point cloud repair network and an improved ICP method is proposed to optimize template matching. The experiment result shows that IPPE-PCR performs better than the state-of-the-art algorithms on LTO-IP. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Intelligent+Manufacturing%22">Journal of Intelligent Manufacturing</searchLink>. Aug2023, Vol. 34 Issue 6, p2797-2807. 11p.
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  Data: <searchLink fieldCode="DE" term="%22Point+cloud%22">Point cloud</searchLink><br /><searchLink fieldCode="DE" term="%22Repairing%22">Repairing</searchLink><br /><searchLink fieldCode="DE" term="%22Everyday+life%22">Everyday life</searchLink>
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  Data: Fast and accurate 6D pose estimation can help a robot arm grab industrial parts efficiently. The previous 6D pose estimation algorithms mostly target common items in daily life. Few algorithms are aimed at texture-less and occluded industrial parts and there are few industrial parts datasets. A novel method called the Industrial Parts 6D Pose Estimation framework based on point cloud repair (IPPE-PCR) is proposed in this paper. A synthetic dataset of industrial parts (SD-IP) is established as the training set for IPPE-PCR and an annotated real-world, low-texture and occluded dataset of industrial parts (LTO-IP) is constructed as the test set for IPPE. To improve the estimation accuracy, a new loss function is used for the point cloud repair network and an improved ICP method is proposed to optimize template matching. The experiment result shows that IPPE-PCR performs better than the state-of-the-art algorithms on LTO-IP. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Intelligent Manufacturing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10845-022-01965-6
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      – Code: eng
        Text: English
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              Text: Aug2023
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