Lightweight-Shaped Object Grasping Detection Network Based on Feature Fusion

Abstract

Robotic grasping techniques for regular targets with known shapes are now well established. However, unknown shaped objects have complex features such as texture, shape, and appearance, which leads to inaccurate recognition and localization of shaped objects during grasp detection. To improve the generalization ability of the grasping detection network for unfamiliar shaped objects, we propose a lightweight shaped object grasping detection network (LSOGD) based on feature fusion, which solves the problem that the network repeatedly extracts features from images and ultimately improves the accuracy of model detection by combining different features. The effectiveness of LSOGD is confirmed by performance evaluation on the Cornell dataset and Jacquard dataset, where the detection accuracy reaches 97.9% and 96.7% for unknown objects, respectively. In addition, due to the small proportion of shaped objects in the current publicly available dataset, we added a portion of industrial-shaped pieces based on the selection of some shaped objects in the Cornell grasping dataset to build a shaped object dataset named X-Cornell on which the accuracy of our proposed model for grasping and detecting the unknown shaped objects is 94.6%. Finally, an actual robot grasping experiment was conducted using a Realsense d435i camera and a Kinova robotic arm, and the success rate of grasping shaped objects was 94%.