Learning-Based Template Matching for Robot Arm Grasping

When applying template matching to the robot arm grasping of rotated objects with high aspect ratios, the accuracy of the matching process is often degraded by the occurrence of high similarity scores with pixels or patches located in neighboring objects. Accordingly, we propose a learning-based template matching (LbTM) algorithm in which the accuracy of the matching results is improved by clearing the matching scores of these confused pixels or patches to zero using a spatial clustering process. This algorithm consists of two modules: First, the translation matching module uses the learning-based pairwise similarity matrix. Having determined the center coordinate of the target object, the second module is applied to estimate the target rotation angle by using a Siamese network. The effectiveness of the proposed algorithm is evaluated for 600 template-rotated target pairs. It is shown that the area under curve (AUC) performance of the proposed algorithm (0.678) is higher than that of three other template matching algorithms (DDIS, CoTM, and QATM). Moreover, our algorithm achieves a minimum success rate of 80% in practical grasping trials performed even using high-aspect objects with various rotational angles and positions.