MASK-GD segmentation based robotic grasp detection

Abstract

Reliability-grasping detection of an object in a complex scene is a challenging task and is a critical problem that needs to be solved urgently in practical application. At present, the grasp position is obtained through the feature analysis of the whole input image. However, the clutter background information in the image impairs the accuracy of grasping detection. In this paper, a robotic grasp detection algorithm named MASK-GD (grasp detection based on mask region) 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 the features of the MASK area rather than the features of the entire image in the scene. It has two stages: the first stage is to provide the MASK area of the target object, and the second stage is a grasp detector based on the features of the MASK area. Experimental results demonstrate that the performance of MASK-GD is comparable with state-of-the-art grasp detection algorithms on Cornell Grasp Dataset and Jacquard Dataset. In the meantime, MASK-GD performs much better in complex scenes.