A Real-Time Robotic Grasping Approach with Oriented Anchor Box

Grasping is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust, and real-Time robotic grasping approach with fully convolutional neural network. The main component of our approach is a grasp detection network with oriented anchor boxes as detection priors. Because the orientation of detected grasps is significant, which determines the rotation angle configuration of the gripper, we propose the orientation anchor box mechanism to regress grasp angle based on predefined assumption instead of classification or regression without any priors. With oriented anchor boxes, the grasps can be predicted more accurately and efficiently. Besides, to accelerate the network training and further improve the performance of angle regression, angle matching is proposed during training instead of Jaccard index matching. Fivefold cross-validation results demonstrate that our proposed algorithm achieves an accuracy of 98.8% and 97.8% in image-wise split and object-wise split, respectively, and the speed of our detection algorithm is 67 frames per second (FPS) with GTX 1080Ti, outperforming all the current state-of-The-Art grasp detection algorithms on Cornell Dataset both in speed and accuracy. Robotic experiments demonstrate the robustness and generalization ability in unseen objects and real-world environment, with the average success rate of 90.0% and 84.2% of familiar things and unseen things, respectively, on Baxter robot platform.