## 4 UAV Experiments
### UAV Experiment Outside Lab  
An outdoor setup was arranged with a vibration table. On this table, a test object was firmly secured. This setup allowed for the adjustment of frequency and amplitude parameters to subject the test object to periodic reciprocating motion. As depicted in Figure 16, there were several stationary background reference points behind the vibration \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ROI** & **Min Pt Region1** & **Max Pt Region1** & **Min Pt Region2** & **Max Pt Region2** \\ \hline Before elimination & 36.6\% & 70.2\% & 53.9\% & 16.6\% \\ After elimination & 98.5\% & 97.8\% & 98.3\% & 98.5\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of the accuracy of the region of interest calculation (1 Hz) for the improved LK method after eliminating UAV wobble. Figure 14: Comparison of Displacement Curves Before and After Drone Noise Cancellation with MTS Output Curves (at 0.5 Hz). Figure 15: Comparison of Displacement Curves Before and After Drone Noise Cancellation with MTS Output Curves (at 1 Hz). table. Two complementary imaging systems were employed for data collection. One was an unmanned aerial vehicle (UAV) fitted with a high-speed camera, and the other was a stationary ground-based camera placed in front of the vibration table. The vibration table produced sinusoidal vibrations at a frequency of 3 Hz and an amplitude of 10 mm. The UAV-mounted camera and the fixed camera recorded videos at frame rates of 60 fps and 30 fps, respectively. At the experimental site, multiple stationary objects were selected as reference targets for calculating the UAV's trajectory. Variations in distances between these stationary objects and the measured object can lead to inaccurate results [33]. To identify the optimal stationary target, comparative analyses were conducted using different stationary objects positioned near the experimental setup. Three stationary objects--the tool bucket (a), cardboard box (b), and iron mesh (c)--were selected for comparative analysis based on their distance relationships to the experimental setup, as illustrated in Figure 17.  
The calculation results using the tool bucket (a) as the stationary target are presented in Figure 18. The UAV motion trajectory derived from this calculation exhibits higher accuracy, with the final displacement curve demonstrating closer alignment to the MTS output. The calculation results using the cardboard box (b) as the stationary target are presented in Figure 19. The derived UAV trajectory exhibits significant errors, because the cardboard box vibrated due to the wind. The final displacement curve shows substantial deviation from the MTS output. Figure 16: Equipment layout of UAV experiment outside lab (a) Tool Bucket; (b) Cardboard Box; (c) Iron Mesh. The calculation results using the iron mesh (c) as the stationary target are presented in Figure 20. The derived UAV trajectory exhibits the largest error, with the final displacement curve demonstrating significant deviation from the MTS output. The possible reason is that iron mesh is too dark to be detected precisely. As presented in Table 8, the precision of calculation results output from the improved LK method (3 Hz) after elimination of UAV trajectories with different reference points are shown. Figure 17: Separate identification of characteristic corner points of selected stationary objects by the program. Figure 18: Comparison of the Displacement Curve After UAV Trajectory Elimination with the MTS Output Curve, Taking the Tool Bucket (a) as the Reference (at 3 Hz). Figure 14:0 Displacement Curve After Eliminating UAV Trajectory Compared with the MTS Output Curve, Using the Cardboard Box (b) as the Reference (at 3 Hz). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stationary Object** & **Tool Bucket (a)** & **Cardboard Box (b)** & **Iron Mesh (c)** \\ \hline After elimination & 95.5\% & 79.7\% & 52.7\% \\ \hline \hline \end{tabular}
\end{table}