Grasping Partially Occluded Objects Using Autoencoder-Based Point Cloud Inpainting
This addresses the challenge of partial occlusion in real-world industrial robotic grasping, enabling more robust object handling, though it is incremental as it builds on existing grasping systems.
The paper tackles the problem of robotic grasping of partially occluded objects in industrial settings by reconstructing missing point cloud information, resulting in a drastic decrease in the number of objects discarded by the process.
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.