AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909609840607232 |
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| author | Lin, Jiong Zhang, Lechen Lee, Kwansoo Ning, Jialong Goldfeder, Judah Lipson, Hod |
| author_facet | Lin, Jiong Zhang, Lechen Lee, Kwansoo Ning, Jialong Goldfeder, Judah Lipson, Hod |
| contents | Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files for unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model that tracks the 6-DoF transformations of point clusters. Through analyzing cluster movements, we hierarchically address the following challenges: (1) moving part segmentation, (2) body topology inference, and (3) joint parameter estimation. The complete pipeline produces robot description files that are fully compatible with existing simulators. We validate our method across a variety of robots, using both synthetic and real-world scan data. Results indicate that our approach outperforms previous methods in registration and body topology estimation accuracy, offering a scalable solution for automated robot modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_05507 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration Lin, Jiong Zhang, Lechen Lee, Kwansoo Ning, Jialong Goldfeder, Judah Lipson, Hod Robotics Computer Vision and Pattern Recognition Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files for unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model that tracks the 6-DoF transformations of point clusters. Through analyzing cluster movements, we hierarchically address the following challenges: (1) moving part segmentation, (2) body topology inference, and (3) joint parameter estimation. The complete pipeline produces robot description files that are fully compatible with existing simulators. We validate our method across a variety of robots, using both synthetic and real-world scan data. Results indicate that our approach outperforms previous methods in registration and body topology estimation accuracy, offering a scalable solution for automated robot modeling. |
| title | AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.05507 |