Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking
This addresses the problem of efficient object grasping in cluttered environments for robotics applications, representing an incremental improvement with a specific method.
The paper tackles robotic bin-picking in cluttered scenarios by using a U-net CNN to combine RGB images and depth information, predicting grasping regions without object recognition or pose estimation, achieving a precision of 95.74% with RGB-Points input.
This paper focuses on a robotic picking tasks in cluttered scenario. Because of the diversity of objects and clutter by placing, it is much difficult to recognize and estimate their pose before grasping. Here, we use U-net, a special Convolution Neural Networks (CNN), to combine RGB images and depth information to predict picking region without recognition and pose estimation. The efficiency of diverse visual input of the network were compared, including RGB, RGB-D and RGB-Points. And we found the RGB-Points input could get a precision of 95.74%.