A novel method for finding grasping handles in a clutter using RGBD Gaussian mixture models. Issue 3 (16th March 2022)

• Record Type: Journal Article
• Authors: Kundu, Olyvia; Dutta, Samrat; Kumar, Swagat
• Is Part Of: Robotica. Volume 40:Issue 3(2022)
• Page Start: 447
• Page End: 463
• Publication Date: 2022-03-16
• Subjects: grasp pose detection -- graspable affordance -- grasping -- RGBD point cloud -- Gaussian mixture model (GMM) -- surface normals -- region growing algorithms -- primitive shape identification; Robots -- Periodicals; 629.89205
• DOI: 10.1017/S0263574721000503
• Languages: English
• ISSNs: 0263-5747

Abstract

Abstract: 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.