用低分辨率深度感知实现低成本可扩展的机器人打包感知
Low-Resolution Perception for Robotic Packing
- 用重建线索指导视点选择，动态更新抓取稳定性估计
- 在极低分辨率下仍能有效避免重复观测，保留关键几何信息
- 适合预算有限、需大规模部署的工业自动化场景
本文针对低成本、低分辨率深度传感下的可扩展机器人打包感知问题提出新框架。该框架通过重建线索驱动下一步视点选择，并利用抓取证据更新单个物体的稳定性估计，共同决定何时采集新数据及何时抓取。重建过程中采用低分辨率下一最佳视点（NBV）策略，明确规避冗余视角，同时保留任务相关几何结构。实验分两步验证：(i) 在极低分辨率条件下对效用函数进行消融研究；(ii) 全端到端评估不同策略表现，证明低分辨率感知是机器人打包中一种实用且可扩展的解决方案。
原文摘要 · Abstract (English)
This work tackles the problem of scalable perception for robotic packing with low-cost, low-resolution depth sensing. We propose a framework where reconstruction cues drive next-view selection and grasp evidence updates a per-object stability estimate, jointly deciding what to acquire next and when to grasp. During the reconstruction, a low-resolution Next Best View (NBV) strategy explicitly avoids redundant views while preserving task-relevant geometry. We validate the approach in two steps: (i) an ablation study of the utility function under very low resolution, and (ii) a full end-to-end evaluation across policies, showing how low-resolution perception is a practical, scalable option for robotic packing.
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