A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models

The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space, such as the bins of a rack in a retail warehouse. The proposed method combines color and depth curvature information to create a Gaussian mixture model that can segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. This helps in overcoming the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that does not require a priori knowledge about object geometry and can be implemented online with near real-time performance. The efficacy of the proposed approach is demonstrated through simulation as well as real-world experiment.

Automated summary

The paper proposes a novel method to detect graspable handles for picking objects from a confined and cluttered space. The method combines color and depth curvature information to segment the target object from its background and imposes the geometrical constraints of a two-finger gripper to localize the graspable regions. The proposed approach overcomes the limitations of a poorly trained deep network object detector and provides a simple and efficient method for grasp pose detection that can be implemented online with near real-time performance.