Uncertainty-based Exploring Strategy in Densely Cluttered Scenes for Vacuum Cup Grasping

Abstract

Grasping a wide range of novel objects in densely cluttered scenes is difficult due to irregular shapes of objects and the uncertainty in sensing. In this paper, a novel vacuum cup grasping method, based on uncertainty modeling of perception data and grasp geometric heuristics, is proposed to grasp unknown objects in densely cluttered scenes. The probabilistic signed distance function is proposed to both reconstruct the point cloud of a scene and explicitly model the uncertainty from depth images captured from a low-cost stereo camera. The quasi-static spring model is used to approximate seal formation between the suction cup and the reconstructed point cloud. A coarse-to-fine exploration procedure is proposed to refine the estimated point cloud, reduce uncertainties during the movement of the robot and redetermine the target grasp pose iteratively. Extensive experiments show that our proposed method achieves state-of-the-art performance on real-world grasping and outperforms existing methods by a large margin.