用低分辨率视角规划机器人打包，效率更高且精度不降。
Low Resolution Next Best View for Robot Packing
- 基于姿态冗余与采集密度的权衡设计新视角选择策略
- 仅用更少视角即达相近重建精度，实测优于传统方法
- 适合对成本和可扩展性要求高的工业自动化场景
在工业自动化中，实现物体的机器人打包是一项关键挑战，高效的目标感知起着基础作用。本文聚焦于无需精确三维重建的场景，优先考虑低成本且可扩展的解决方案。提出的低分辨率最优下一步视角（LR-NBV）算法，利用一个平衡姿态冗余与采集密度的效用函数，确保高效的物体重建。实验验证表明，LR-NBV在所有测试条件下均持续优于标准的下一最佳视角（NBV）方法，在显著减少视角数量的同时保持相近的重建精度。该方法证明了在不依赖高精度传感的前提下，对效率、可扩展性和适应性有较高要求的应用场景中具有高度适用性。
原文摘要 · Abstract (English)
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.
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