Low Resolution Next Best View for Robot Packing
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917222137462784 |
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| author | Preziosa, Giuseppe Fabio Castellano, Chiara Zanchettin, Andrea Maria Faroni, Marco Rocco, Paolo |
| author_facet | Preziosa, Giuseppe Fabio Castellano, Chiara Zanchettin, Andrea Maria Faroni, Marco Rocco, Paolo |
| contents | Automating the packing of objects with robots is a key challenge in industrial automation, where efficient object perception plays a fundamental role. This paper focuses on scenarios where precise 3D reconstruction is not required, prioritizing cost-effective and scalable solutions. The proposed Low-Resolution Next Best View (LR-NBV) algorithm leverages a utility function that balances pose redundancy and acquisition density, ensuring efficient object reconstruction. Experimental validation demonstrates that LR-NBV consistently outperforms standard NBV approaches, achieving comparable accuracy with significantly fewer poses. This method proves highly suitable for applications requiring efficiency, scalability, and adaptability without relying on high-precision sensing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_04228 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low Resolution Next Best View for Robot Packing Preziosa, Giuseppe Fabio Castellano, Chiara Zanchettin, Andrea Maria Faroni, Marco Rocco, Paolo Robotics Computer Vision and Pattern Recognition Automating the packing of objects with robots is a key challenge in industrial automation, where efficient object perception plays a fundamental role. This paper focuses on scenarios where precise 3D reconstruction is not required, prioritizing cost-effective and scalable solutions. The proposed Low-Resolution Next Best View (LR-NBV) algorithm leverages a utility function that balances pose redundancy and acquisition density, ensuring efficient object reconstruction. Experimental validation demonstrates that LR-NBV consistently outperforms standard NBV approaches, achieving comparable accuracy with significantly fewer poses. This method proves highly suitable for applications requiring efficiency, scalability, and adaptability without relying on high-precision sensing. |
| title | Low Resolution Next Best View for Robot Packing |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.04228 |