Low Resolution Next Best View for Robot Packing

2025-05-07

AI Summary

Industrial robotic bin-packing tasks demand cost-effective and highly scalable object perception without relying on high-fidelity 3D reconstruction. Method: This paper proposes a Low-Resolution Next-Best-View (LR-NBV) approach that jointly optimizes pose redundancy and acquisition density within the NBV framework—introducing, for the first time, a utility function explicitly designed for low-resolution depth imagery. The method integrates lightweight geometric feature modeling with incremental view selection to enable efficient and robust perception planning. Contribution/Results: Experiments demonstrate that LR-NBV significantly reduces the number of required viewpoints compared to standard NBV while achieving comparable 3D reconstruction accuracy. Validation on a real robotic platform confirms superior computational efficiency, enhanced scalability across diverse object geometries and quantities, and improved adaptability to varying environmental conditions.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Automating robot packing with efficient perception
• Balancing cost and scalability in 3D reconstruction
• Improving NBV efficiency with low-resolution sensing

Innovation

• LR-NBV algorithm balances pose redundancy and density
• Achieves accuracy with fewer poses than standard NBV
• Cost-effective scalable solution without high-precision sensing