Mask-GD Segmentation Based Robotic Grasp Detection
This addresses a critical issue for robotic applications by improving grasp detection accuracy in cluttered environments, though it is an incremental improvement over existing methods.
The paper tackled the problem of unreliable grasp detection in complex scenes by proposing MASK-GD, a two-stage algorithm that uses segmented object masks instead of whole-image features, achieving performance comparable to state-of-the-art methods on standard datasets and better results in complex scenes.
The reliability of grasp detection for target objects in complex scenes is a challenging task and a critical problem that needs to be solved urgently in practical application. At present, the grasp detection location comes from searching the feature space of the whole image. However, the cluttered background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD is proposed, which provides a feasible solution to this problem. MASK is a segmented image that only contains the pixels of the target object. MASK-GD for grasp detection only uses MASK features rather than the features of the entire image in the scene. It has two stages: the first stage is to provide the MASK of the target object as the input image, and the second stage is a grasp detector based on the MASK feature. Experimental results demonstrate that MASK-GD's performance is comparable with state-of-the-art grasp detection algorithms on Cornell Datasets and Jacquard Dataset. In the meantime, MASK-GD performs much better in complex scenes.