Combining RGB and Points to Predict Grasping Region for Robotic Bin-Picking

Understand

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.