Low Resolution Next Best View for Robot Packing
This work addresses cost-effective and scalable automation for industrial packing, though it appears incremental as it builds on existing next best view methods.
The paper tackled the problem of efficient object perception for robot packing by proposing a low-resolution next best view algorithm, which achieved comparable accuracy with significantly fewer poses than standard approaches.
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.