# RGB-D-Based Robotic Grasping in Fusion Application Environments
## 3 Results
### Quantitative Evaluation  
In this subsection, we will compare the success grasping rate of our method with the GR-ConvNet and UOIS methods. The GR-ConvNet is an end-to-end grasping network that outputs a rectangle representing the grasping pose and width. UOIS is the fundamental instance segmentation network of our method. In the experiment, we calculate the grasping success rate of the UOIS method by calculating the grasping pose directly from the segmentation results output by the network.  
To better simulate the environment in VV, the object to be grasped was placed on a metal table in this experiment, as shown in Figure 5. To compare the effect of different materials of the background environment, we also conduct comparative experiments on wooden tables. We performed 100 grasping operations each with these methods under the same scenarios and then calculated the success rate. These scenarios include two different poses: standing pose, lying down pose and two different backgrounds: wooden table and metal table, as shown in Figure 7.  
Figure 6: The images from the RGB-D camera’s view of the various stages in the experiment.  
Figure 7: Experimental scenarios, including different backgrounds: wooden table and metal table, and different poses: standing pose and lying pose.  
The results in Table 1 show that the different backgrounds make a big difference in the results. If the background and the object are very close in material and color, it is difficult for these networks to identify the object correctly. Meanwhile, the performance of the GR-Convent for the standing poses is much poorer than the lying ones. This is because, when the object is standing on the table, the height difference of the object becomes more significant, and the error in the depth information from the RGB-D camera becomes more prominent, especially for the cylindrical part at the bottom. On the other hand, GR-Convent is more likely to choose the cylindrical part as the grasping target, which means it could hardly find an appropriate grasping pose.  
However, the UOIS and the method proposed in this paper are based on instance segmentation. When the object is in its standing pose, the instance segmentation network in these methods could better identify the top plane of the object. Thus, this is why these methods' performance on the standing pose are better than the lying pose.  
Another factor that influences the proposed method's performance on the lying pose is that, when the object is lying down, the sides of the cylinder would have some adverse effects on the clustering algorithm.  
Overall, our method performed much better under different scenarios. In particular, our method still achieved a 97% success grasping rate in the closest conditions to the actual situation (metal table plus standing pose). Most importantly, when our algorithm does not perform well, we can find which part goes wrong and improve it. For our method, the failure cases during the grasp are because of the error of the clustering results. We can adjust the parameters \(\epsilon\) and \(MinPts\) for different situations. The tuning of these parameters can intuitively change the performance of the algorithm for different cases; for example, if we want the algorithm to be more conservative, we also reduce the value of \(MinPts\). It makes our approach more reliable and controllable in this challenging environment.