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.
@article{arxiv.2505.04228,
title = {Low Resolution Next Best View for Robot Packing},
author = {Giuseppe Fabio Preziosa and Chiara Castellano and Andrea Maria Zanchettin and Marco Faroni and Paolo Rocco},
journal= {arXiv preprint arXiv:2505.04228},
year = {2026}
}