## 5 Conclusions  
In this study, a vision system is proposed for the object-handling tasks of UAVs. The system combines 2D images and 3D point cloud information to accurately detect and segment various objects in complex scenes and realize 6D object pose estimation, which can provide a solid foundation for intelligent picking or other object handling tasks of UAVs. In addition, to evaluate the performance of the system more comprehensively, we contribute a 6D pose estimation dataset named the SIAT dataset. Experiments conducted on the SIAT dataset and benchmark YCB-Video dataset demonstrate the robustness of our system. Currently, the proposed method experiences slow image transmission. Upgrading the hardware to improve image transmission speed and researching more lightweight networks and algorithms to carry vision systems on UAVs to complete offline 6D pose estimation will be considered in future works. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline  & **Detection** & **Segmentation** & **Classification** & \multicolumn{2}{c}{**6D Pose**} & \multicolumn{2}{c}{**Image**} \\  & **Detection** & **Segmentation** & **Classification** & \multicolumn{2}{c}{**Estimation**} & \multicolumn{2}{c}{**Transmission**} \\ \hline Time (s) & 0.049 & 0.02 & 0.002 & 0.023 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Running time of all networks used in the vision system and the time of image transmission. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**PoseCNN + ICP**} & \multicolumn{2}{c}{**Dense(per)**} & \multicolumn{2}{c}{**Dense(iter)**} & \multicolumn{2}{c}{**Ours(per)**} & \multicolumn{2}{c}{**Ours(iter)**} \\ \cline{2-11}  & **AUC** & **ADD-S** & **AUC** & **ADD-S** & **AUC** & **ADD-S** & **AUC** & **ADD-S** & **AUC** & **ADD-S** \\ \hline
002 master chef can & 95.8 & 100.0 & 95.2 & 100.0 & 96.4 & 100.0 & 95.3 & 100.0 & 96.3 & 100.0 \\ \hline
003 cracker box & 92.7 & 93.0 & 92.5 & 99.3 & 95.5 & 99.5 & 92.6 & 100.0 & 96.3 & 100.0 \\ \hline
004 sugar box & 98.2 & 100 & 95.1 & 100.0 & 97.5 & 100.0 & 95.5 & 100.0 & 97.7 & 100.0 \\ \hline
005 tomato soup can & 94.5 & 96.8 & 93.7 & 96.9 & 94.6 & 96.9 & 96.8 & 100.0 & 97.7 & 100.0 \\ \hline
006 mustard bottle & 98.6 & 100.0 & 95.9 & 100.0 & 97.2 & 100.0 & 96.0 & 100.0 & 97.8 & 100.0 \\ \hline
007 tuna fish can & 97.1 & 97.9 & 94.9 & 100.0 & 96.6 & 100.0 & 96.0 & 100.0 & 97.2 & 100.0 \\ \hline
008 pudding box & 97.9 & 100.0 & 94.7 & 100.0 & 96.5 & 100.0 & 94.3 & 100.0 & 96.8 & 100.0 \\ \hline
009 gelatin box & 98.8 & 100.0 & 95.8 & 100.0 & 98.1 & 100.0 & 97.3 & 100.0 & 98.2 & 100.0 \\ \hline
010 potted meat can & 92.7 & 97.2 & 90.1 & 93.1 & 91.3 & 93.1 & 93.0 & 95.4 & 94.0 & 95.3 \\ \hline
011 banana & 97.1 & 99.7 & 91.5 & 93.9 & 96.6 & 100.0 & 93.5 & 96.8 & 97.1 & 100.0 \\ \hline
019 pitcher base & 97.8 & 100.0 & 94.6 & 100.0 & 97.1 & 100.0 & 93.4 & 99.5 & 97.9 & 100.0 \\ \hline
021 bleach cleanser & 96.9 & 99.9 & 94.3 & 99.8 & 95.8 & 100.0 & 95.0 & 99.7 & 96.7 & 100.0 \\ \hline
024 bowl & 81.0 & 58.8 & 86.6 & 69.5 & 88.2 & 98.8 & 84.4 & 73.9 & 88.8 & 96.8 \\ \hline
025 mug & 95.0 & 99.5 & 95.5 & 100.0 & 97.1 & 100.0 & 96.0 & 100.0 & 97.3 & 100.0 \\ \hline
035 power drill & 98.2 & 99.9 & 92.4 & 97.1 & 96.0 & 98.7 & 92.9 & 97.3 & 96.1 & 98.3 \\ \hline
036 wood block & 87.6 & 82.6 & 85.5 & 93.4 & 89.7 & 94.6 & 85.8 & 84.3 & 91.7 & 96.7 \\ \hline
037 scissors & 91.7 & 100 & 96.4 & 100.0 & 95.2 & 100.0 & 96.6 & 100.0 & 93.1 & 99.5 \\ \hline
040 large marker & 97.2 & 98.0 & 94.7 & 99.2 & 97.5 & 100.0 & 95.9 & 99.7 & 97.8 & 100.0 \\ \hline
051 large clamp & 75.2 & 75.6 & 71.6 & 78.5 & 72.9 & 79.2 & 73.7 & 79.2 & 75.7 & 80.1 \\ \hline
052 extra large clamp & 64.4 & 55.6 & 69.0 & 69.5 & 69.8 & 76.3 & 83.4 & 83.6 & 83.3 & 88.9 \\ \hline
061 foam brick & 97.2 & 99.6 & 92.4 & 100.0 & 92.5 & 100.0 & 94.8 & 100.0 & 96.4 & 100.0 \\ \hline MEAN & 93.0 & 93.1 & 91.2 & 95.3 & 93.1 & 96.8 & 92.8 & 96.5 & 94.8 & 97.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Quantitative evaluation of pose estimation on YCB-Video dataset.