Low Resolution Next Best View for Robot Packing

TL;DR

This paper introduces LR-NBV, a low-resolution next best view algorithm that enhances robot packing efficiency by reducing the number of required poses while maintaining accuracy, suitable for scalable industrial automation.

Contribution

The paper presents a novel low-resolution NBV method that balances pose redundancy and acquisition density, improving efficiency in object perception for robot packing.

Findings

LR-NBV outperforms standard NBV in efficiency

Achieves comparable accuracy with fewer poses

Suitable for scalable, cost-effective automation

Abstract

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.