Lightweight Robotic Grasping Model Based on Template Matching and Depth Image

This letter proposes a lightweight DNN model for robotic grasping applications with just 1.5 million architecture parameters. In the proposed model, the location of the target object is estimated using a pairwise template matching method, while the orientation of the object is predicted using depth images and a convolutional neural network (CNN). The feasibility of the proposed model is demonstrated both numerically and experimentally on an NVIDIA Jetson NX developer kit. The experimental results show that the grasping system achieves an accuracy of 96.3% and a running time of 125 ms when on 700 images. Moreover, when applied to practical grasping tasks on 20 unseen objects selected from the Cornell grasping dataset, the system achieves an accuracy of 92.5%, which is comparable to that of existing state-of-the-art methods reported in the literature.