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.

In 6D object pose estimation task, object models are usually available and represented as the point cloud set in canonical object frame, which are important references for estimating object poses to the camera frame. However, directly introducing object models as the prior knowledge (i.e., object model point cloud) will cause potential perturbations and even degenerate pose estimation performance. To make the most of object model priors and eliminate the problem, we present an end-to-end deep learning approach called the Geometric Constraint Co-attention Network (GCCN) for 6D object pose estimation. GCCN is designed to explicitly leverage the object model priors effectively with the co-attention mechanism. We add explicit geometric constraints to a co-attention module to inform the geometric correspondence relationships between points in the scene and object model priors and develop a novel geometric constraint loss to guide the training. In this manner, our method effectively eliminates the side effect of directly introducing the object model priors into the network. Experiments on the YCB-Video and LineMOD datasets demonstrate that our GCCN substantially improves the performance of pose estimation and is robust against heavy occlusions. We also demonstrate that GCCN is accurate and robust enough to be deployed in real-world robotic tasks.