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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 09565515 |
| DOI: | 10.1007/s10845-022-01965-6 |
