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arXiv · 2311.04645

SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store

Biqi Yang·Weiliang Tang·Xiaojie Gao·Xianzhi Li·Yun-Hui Liu·Chi-Wing Fu·Pheng-Ann Heng
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Abstract

In large-scale storehouses, precise instance masks are crucial for robotic bin picking but are challenging to obtain. Existing instance segmentation methods typically rely on a tedious process of scene collection, mask annotation, and network fine-tuning for every single Stock Keeping Unit (SKU). This paper presents SKU-Patch, a new patch-guided instance segmentation solution, leveraging only a few image patches for each incoming new SKU to predict accurate and robust masks, without tedious manual effort and model re-training. Technical-wise, we design a novel transformer-based network with (i) a patch-image correlation encoder to capture multi-level image features calibrated by patch information and (ii) a patch-aware transformer decoder with parallel task heads to generate instance masks. Extensive experiments on four storehouse benchmarks manifest that SKU-Patch is able to achieve the best performance over the state-of-the-art methods. Also, SKU-Patch yields an average of nearly 100% grasping success rate on more than 50 unseen SKUs in a robot-aided auto-store logistic pipeline, showing its effectiveness and practicality.

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Biqi Yang, Weiliang Tang, Xiaojie Gao, Xianzhi Li, Yun-Hui Liu, Chi-Wing Fu, Pheng-Ann Heng. 2023-11-08. SKU-Patch: Towards Efficient Instance Segmentation for Unseen Objects in Auto-Store. https://arxiv.org/abs/2311.04645

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