# Object-Environment Fusion of Visual System for Automatic Pear Picking
## 3 Results
### Picking Pose Estimation Experiment
#### 3.3.2 Experimental Results  
To verify the effectiveness of the proposed method (Our mtd) for picking pose estimation, it was compared with Frustum-B [29] and VPM [25]. The operating distance was divided into three groups: 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm. Table 3 presents the results for the different methods. From Table 3, it can be seen that our method had significant advantages in the metric of SR. Our method was capable of offering multiple collision-free picking poses, but Frustum-B and VPM were limited to providing one solution and lacked collision checks. This demonstrates that our method has better adaptability in the field. In the S1 dataset, the SRs of our method within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm were 92.11%, 93.02%, and 90.21%, respectively. In the S2 dataset, the SRs of our method within the ranges of 30\(\sim\)50 cm, 50\(\sim\)90 cm, and 90\(\sim\)120 cm were 81.24%, 87.11%, and 76.22%, respectively. Moreover, Table 3 indicates that the distance from the object to the camera exerted a considerable impact on the picking pose performance. The proposed method, Frustum-B, and VPM all achieved the best performance in the range of 50\(\sim\)90 cm. Moreover, the variations in the metrics of MeanE and MedE were notably minor. The analysis conducted in this section revealed that the depth data were predominantly influenced by the camera, which posed a challenge for refinement by the algorithms. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{3}{c}{**S1 Dataset**} & \multicolumn{3}{c}{**S2 Dataset**} \\ \hline
**Method** & **Distance** & **SR** & **MeanE** & **MedE** & **SR** & **MeanE** & **MedE** \\ \hline \multirow{3}{*}{**Our method**} & 30\(\sim\)50 cm & 92.11\% & 1.87 cm & 2.18 cm & 81.24\% & 3.04 cm & 4.18 cm \\  & 50\(\sim\)90 cm & 93.02\% & 1.85 cm & 2.10 cm & 87.11\% & 2.61 cm & 3.94 cm \\  & 90\(\sim\)120 cm & 90.21\% & 1.90 cm & 2.21 cm & 76.22\% & 3.27 cm & 4.47 cm \\ \hline \multirow{3}{*}{**Frustum-B**} & 30\(\sim\)50 cm & 89.83\% & 1.59 cm & 1.78 cm & 71.90\% & 2.54 cm & 3.52 cm \\  & 50\(\sim\)90 cm & 91.01\% & 1.53 cm & 1.32 cm & 75.34\% & 2.20 cm & 3.39 cm \\  & 90\(\sim\)120 cm & 88.54\% & 1.63 cm & 1.80 cm & 70.62\% & 2.69 cm & 3.76 cm \\ \hline \multirow{3}{*}{**VPM**} & 30\(\sim\)50 cm & 87.31\% & 1.82 cm & 2.05 cm & 70.73\% & 2.55 cm & 3.95 cm \\  & 50\(\sim\)90 cm & 89.21\% & 1.71 cm & 1.96 cm & 74.85\% & 2.39 cm & 3.73 cm \\ \cline{1-1}  & 90\(\sim\)120 cm & 87.43\% & 1.89 cm & 2.10 cm & 69.29\% & 2.73 cm & 4.11 cm \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of SR, MeanE, and MedE of picking pose estimation among different methods. Our method utilized the center of the object's point cloud as the picking position. Meanwhile, Frustum-B and VPM employed fitting algorithms to ascertain the picking position. In the case of incomplete depth and object masks, there were no significant disparities in performance among our method, Frustum-B, and VPM. Furthermore, even in cases where the depth and masks of the object were complete, despite the fitting algorithms offering certain benefits, they did not yield substantial improvements. In the S1 dataset, the MeanE and MedE of our method were 1.85 cm and 2.10 cm within the range of 50\(\sim\)90 cm, respectively. In the S2 dataset, the MeanE and MedE of our method were 2.61 cm and 3.94 cm within the range of 50\(\sim\)90 cm, respectively. Figure 15 displays the picking pose generated by our method, FrustumB, and VPM. It can be seen that our method provided multiple collision-free picking candidates for the object. Any one of these candidates could feasibly be used as the picking pose for real operation. Frustum-B and VPM demonstrated the accurate estimation of the picking position, but they each provided only one candidate. This limitation arose from their failure to consider the influence of obstacles on picking, which could lead to a collision.