Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation*
Bin-picking of Randomly Piled Shiny Industrial Objects Using Light Transport Matrix Estimation*
复制标题
使用光传输矩阵估计对随机堆放的闪亮工业物体进行装箱拣选*
DOI：
10.1109/robio49542.2019.8961622
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Koichi Hashimoto
中科院分区：
文献类型：
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作者：
Naoya Chiba;Mingyu Li;Akira Imakura;Koichi Hashimoto
在自动化工厂中使用机器人需要准确的装箱，以确保正确识别和选择物体。在物体表面有多个反射的情况下，这是一项具有挑战性的任务。我们试图通过开发一种基于光传输矩阵（LTM）的3D测量方法来解决这个问题，该方法可以应用于发光物体或半透明物体。本文提出的研究通过检查垃圾箱拾取任务（工厂自动化中众所周知的机器人应用）来评估所提出的方法以及我们之前报告的3D姿态估计方法的准确性。对于用于一般物体的自动拣箱系统存在相当大的需求。然而，在使用具有成本效益的测量系统时，由于难以准确测量它们的形状，一些物体（诸如闪亮的金属物体）在拣箱方面继续证明是有问题的。我们的3D测量方法仅使用投影仪-相机系统;因此，它具有成本效益，并且不需要任何特殊的光学系统。它是基于快速LTM稀疏估计。我们以前证明了这种方法可以测量金属物体的3D形状，并表明我们的姿态估计方法适用于装箱。然而，我们没有通过3D机器人视觉的应用验证其准确性。在这项研究中，我们整合了这两种方法，并证明我们的3D测量方法，结合我们的姿态估计工作，可以成功地完成涉及闪亮的金属工业物体的拣箱任务。最终，我们在5个场景（包括15件作品）中取得了100 [%]的采摘成功率。我们的结论是，我们提出的方法是足够准确的自动化工厂环境中进行装箱任务。
The use of robots in automated factories requires accurate bin-picking to ensure that objects are correctly identified and selected. In the case of objects with multiple reflections from their surfaces, this is a challenging task. We attempted to address this problem by developing a 3D measurement method based on a Light Transport Matrix (LTM), which can be applied to shiny objects or semi-transparent objects. The study presented herein evaluates the accuracy of the proposed method as well as the method for 3D pose estimation we previously reported, by examining a bin-picking task, which is a well-known robot application in factory automation. There is considerable demand for automated bin-picking systems for general objects. However, in the use of cost-effective measurement systems, some objects such as shiny metallic objects continue to prove problematic in terms of bin-picking because of the difficulty to measure their shapes accurately. Our 3D measurement method uses only a projector-camera system; thus, it is cost-effective, and it does not require any special optical system. It is based on fast LTM sparse estimation. We previously demonstrated that this approach can measure the 3D shape of metallic objects and showed that our pose estimation method is applicable to bin-picking. However, we did not verify its accuracy with the application of 3D robot vision. In this study, we integrate these two methods, and demonstrate that our 3D measurement method, in combination with our pose estimation work, can successfully accomplish bin-picking tasks involving shiny metallic industrial objects. Ultimately, we achieved 100 [%] picking success for 5 scenes including 15 pieces. We concluded that our proposed methods are sufficiently accurate to carry out bin-picking tasks in automated factory environments.