日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

週刊ニュースレター購読
マニピュレーションarXiv:2503.12549

オートエンコーダによる点群補完を用いた部分遮蔽物体の把持

Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting

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部分遮蔽された物体の点群をオートエンコーダで補完し、把持点計算を可能にする手法を提案。実産業応用で廃棄される物体数を大幅に削減した。

著者: Alexander Koebler, Ralf Gross, Florian Buettner, Ingo Thon

分類: cs.RO, cs.AI, cs.CV

原文アブストラクト

Flexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such systems is the robotic grasping of known or unknown objects in random positions. Real-world applications often come with challenges that might not be considered in grasping solutions tested in simulation or lab settings. Partial occlusion of the target object is the most prominent. Examples of occlusion can be supporting structures in the camera's field of view, sensor imprecision, or parts occluding each other due to the production process. In all these cases, the resulting lack of information leads to shortcomings in calculating grasping points. In this paper, we present an algorithm to reconstruct the missing information. Our inpainting solution facilitates the real-world utilization of robust object matching approaches for grasping point calculation. We demonstrate the benefit of our solution by enabling an existing grasping system embedded in a real-world industrial application to handle occlusions in the input. With our solution, we drastically decrease the number of objects discarded by the process.

関連論文

PR本紙発行元 EmplifAI