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| Format: | Preprint |
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2024
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| Online-Zugang: | https://arxiv.org/abs/2403.12449 |
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| _version_ | 1866916165965578240 |
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| author | Lim, Seunghyeon Yoo, Youngjae Lee, Jun Ki Zhang, Byoung-Tak |
| author_facet | Lim, Seunghyeon Yoo, Youngjae Lee, Jun Ki Zhang, Byoung-Tak |
| contents | In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_12449 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter Lim, Seunghyeon Yoo, Youngjae Lee, Jun Ki Zhang, Byoung-Tak Robotics In this paper, we propose a novel method for plane clustering specialized in cluttered scenes using an RGB-D camera and validate its effectiveness through robot grasping experiments. Unlike existing methods, which focus on large-scale indoor structures, our approach -- Multi-Object RANSAC emphasizes cluttered environments that contain a wide range of objects with different scales. It enhances plane segmentation by generating subplanes in Deep Plane Clustering (DPC) module, which are then merged with the final planes by post-processing. DPC rearranges the point cloud by voting layers to make subplane clusters, trained in a self-supervised manner using pseudo-labels generated from RANSAC. Multi-Object RANSAC demonstrates superior plane instance segmentation performances over other recent RANSAC applications. We conducted an experiment on robot suction-based grasping, comparing our method with vision-based grasping network and RANSAC applications. The results from this real-world scenario showed its remarkable performance surpassing the baseline methods, highlighting its potential for advanced scene understanding and manipulation. |
| title | Multi-Object RANSAC: Efficient Plane Clustering Method in a Clutter |
| topic | Robotics |
| url | https://arxiv.org/abs/2403.12449 |