# Aerial Robotic Solution for Detailed Inspection of Viaducts
## 4 Experimental Results
### Inspection Result  
The results obtained after performing a mission show the tracking of the planned trajectory by taking images of the viaduct. Each image obtained saves metadata containing the exact pose and instant in which they were taken with respect to the 3D map of the viaduct. In this way, it is always possible to review the inspection performed and to know the exact location to which each image belongs, with respect to the 3D map. Figure 12 illustrates one of the experiments performed. Specifically, it refers to Algodonales sequence 2, where a flight was performed under the viaduct deck, along the external part of the viaduct. Each vertical arrow indicates the pose of the camera for each acquired image. The green line represents the trajectory of the AERO-CAM.  
The pictures shown in Figure 12b,c show an example of the visual information obtained with the AERO-CAM. Both pictures show different parts of the lower part of the viaduct deck, which is a difficult area to access. Figure 12b focuses on one of the cross beams, while picture Figure 12c shows the outer side of the deck. As explained above, the quality and level of detail of these pictures depend on the choice and configuration of the camera as well as the distance to the structure configured in the mission. In this case, an 85 mm lens has been used with a distance to the structure of about 2 m. This results in pictures with a resolution of 9504 \(\times\) 6336 pixels in which the detail density per pixel is very high.  
The distribution of the pictures along the structure is also crucial for a possible later analysis by performing a reconstruction of the structure through photogrammetry. All pictures have enough overlap between them to make this possible. The overlap is not only beneficial for possible photogrammetry but also allows the same point to be analyzed from different pictures, adding more redundancy and information to the system.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multicolumn{2}{c}{**Description**} & \multicolumn{4}{c}{**APE [m]**} & \multicolumn{4}{c}{**RPE [m] (\(\Delta=0.5\) m)**} \\ \cline{2-10}  & **Dist [m]** & **Time [s]** & **Max** & **Mean** & **Std** & **RMSE** & **Max** & **Mean** & **Std** & **RMSE** \\ \hline seq0 & 93.18 & 260.52 & 0.44 & 0.12 & 0.08 & 0.14 & 0.49 & 0.16 & 0.1 & 0.19 \\ \hline seq1 & 204.47 & 486.57 & 0.85 & 0.14 & 0.09 & 0.17 & 1.35 & 0.16 & 0.08 & 0.18 \\ \hline seq2 & 119.83 & 334.97 & 0.38 & 0.12 & 0.06 & 0.13 & 0.35 & 0.13 & 0.06 & 0.14 \\ \hline seq4 & 280.93 & 547.72 & 0.66 & 0.17 & 0.08 & 0.18 & 10.36 & 0.18 & 0.31 & 0.36 \\ \hline seq5 & 324.76 & 644.37 & 0.73 & 0.16 & 0.09 & 0.18 & 0.48 & 0.17 & 0.11 & 0.2 \\ \hline seq6 & 108.96 & 211.71 & 0.79 & 0.13 & 0.12 & 0.18 & 0.57 & 0.23 & 0.12 & 0.26 \\ \hline seq7 & 113.4 & 160.51 & 0.75 & 0.18 & 0.13 & 0.22 & 0.69 & 0.25 & 0.11 & 0.27 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Alora dataset description and localization errors.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Seq**} & \multicolumn{2}{c}{**Description**} & \multicolumn{4}{c}{**APE [m]**} & \multicolumn{4}{c}{**RPE [m] (\(\Delta=0.5\) m)**} \\ \cline{2-10}  & **Dist [m]** & **Time [s]** & **Max** & **Mean** & **Std** & **RMSE** & **Max** & **Mean** & **Std** & **RMSE** \\ \hline seq0 & 175.65 & 376.7 & 0.49 & 0.11 & 0.07 & 0.13 & 1.1 & 0.25 & 0.2 & 0.32 \\ \hline seq1 & 227.92 & 397.85 & 0.45 & 0.13 & 0.06 & 0.14 & 0.94 & 0.4 & 0.2 & 0.45 \\ \hline seq2 & 182.02 & 419.7 & 0.36 & 0.12 & 0.05 & 0.13 & 1.0 & 0.54 & 0.27 & 0.61 \\ \hline seq3 & 276.3 & 526.14 & 1.04 & 0.36 & 0.16 & 0.4 & 1.62 & 0.56 & 0.25 & 0.61 \\ \hline seq4 & 251.91 & 515.85 & 0.52 & 0.12 & 0.06 & 0.14 & 0.99 & 0.54 & 0.23 & 0.59 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Algodonales dataset description and localization errors.