A novel integrated method of detection-grasping for specific object
  based on the box coordinate matching
Zongmin Liu Jirui Wang Jie Li Zufeng Li Kai Ren Peng Shi
To better care for the elderly and disabled, it is essential for service
robots to have an effective fusion method of object detection and grasp
estimation. However, limited research has been observed on the combination of
object detection and grasp estimation. To overcome this technical difficulty, a
novel integrated method of detection-grasping for specific object based on the
box coordinate matching is proposed in this paper. Firstly, the SOLOv2 instance
segmentation model is improved by adding channel attention module (CAM) and
spatial attention module (SAM). Then, the atrous spatial pyramid pooling (ASPP)
and CAM are added to the generative residual convolutional neural network
(GR-CNN) model to optimize grasp estimation. Furthermore, a detection-grasping
integrated algorithm based on box coordinate matching (DG-BCM) is proposed to
obtain the fusion model of object detection and grasp estimation. For
verification, experiments on object detection and grasp estimation are
conducted separately to verify the superiority of improved models.
Additionally, grasping tasks for several specific objects are implemented on a
simulation platform, demonstrating the feasibility and effectiveness of DG-BCM
algorithm proposed in this paper.