用点云修复技术让机器人在遮挡下仍能精准抓取物体
Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
- 基于自编码器的点云补全方法，重建被遮挡部分的几何信息
- 在真实工业场景中使被丢弃的物体数量显著减少
- 适合需要应对复杂遮挡的工业机器人抓取系统
柔性工业生产系统将在未来制造中发挥核心作用，因其支持更高程度的产品个性化与定制化。其中关键环节是机器人对已知或未知物体在随机位置的抓取。现实应用常面临仿真或实验室环境未考虑的挑战，其中最突出的是目标物体的部分遮挡，如相机视野中的支撑结构、传感器误差或生产过程中零件间的相互遮挡。这些情况导致信息缺失，影响抓取点计算。本文提出一种算法以重建缺失信息。我们的补全方案使鲁棒的物体匹配方法能应用于实际抓取点计算。通过将现有抓取系统嵌入真实工业应用，我们验证了该方法在处理输入遮挡方面的有效性。使用本方案后，流程中被丢弃的物体数量大幅下降。
原文摘要 · Abstract (English)
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.
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