# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 3 Results
#### Procedure Validation
On the other hand, for UAV photogrammetry, the maximum absolute error was 0.190 m between points T6 and T1, and the absolute minimum error was 0.006 m between T5 and T6. Likewise, the RMSE of the LiDAR and UAV photogrammetry was calculated against the values obtained with the TS. LiDAR presented an RMSE of 0.171 m, while photogrammetry presented an RMSE of 0.024 m.  
Similarly, in the LiDAR-UAV system proposed for the reconstruction of urban environments, it was found that the system reached an accuracy of approximately 1 m compared to the results of a laser scanner (TLS) [1]. These results are in the range of the accuracy errors in the presented investigation. Additionally, the system developed to observe the production of crops [20] with software materials like the ones applied to this investigation presented a variation in the height of the crops between 0.35 and 0.58 m. The use of the UAV-LiDAR system for height estimation of different crops showed an RSME of 0.034, 0.074, and 0.12 m for wheat, sugar beet, and potato, respectively [7]. The information from both point clouds is similar, and both show pros and cons. For example, although photogrammetry reads the area with greater detail in textures, it is affected by light and shadows. On the other hand, LiDAR is not affected by weather conditions. LiDAR is also more effective than photogrammetry in areas with dense vegetation or where the surface is complex to assess visually [37] and is better suited for large areas, as, for more detailed surveys, it would be preferred to consider adding more conventional topographic techniques as a compliment [36], like the GCPs obtained with GNSS and total station used in this investigation. A comparison between this two point clouds is shown in Figure 22. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{6}{c}{**Control Point Errors**} \\ \hline \multicolumn{6}{c}{**LiDAR**} \\ \hline
**Point** & \(\Delta\)**X (m)** & \(\Delta\)**Y (m)** & \(\Delta\)**Z (m)** & **RMSE (m)** \\ \hline T1 & \(-0.2921\) & \(-0.7855\) & 0.7220 & \\ T2 & \(-0.1950\) & \(-0.9230\) & 0.5020 & \\ T3 & 0.3190 & \(-0.5770\) & 0.5220 & \\ T4 & 0.0330 & 0.0000 & 0.1160 & \\ T5 & \(-0.2850\) & \(-0.1030\) & 0.1570 & \\ T6 & \(-0.1720\) & \(-0.3970\) & 0.3620 & 0.766 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Corrected control point coordinate errors. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Point Cloud** & \multicolumn{6}{c}{**Measure (m)**} \\ \hline
**T1-T2** & **T2-T3** & **T3-T4** & **T4-T5** & **T5-T6** & **T6-T1** & **RMSE (m)** \\ \hline Total Station (Reference) & 9.952 & 10.001 & 64.771 & 10.008 & 9.924 & 64.668 & \\ LiDAR & 10.052 & 10.168 & 65.118 & 9.713 & 10.06 & 64.837 & \\ Error between TS and LiDAR & \(-0.100\) & \(-0.167\) & \(-0.347\) & 0.295 & \(-0.136\) & \(-0.169\) & 0.171 \\ UAV Photogrammetry & 9.939 & 9.965 & 64.711 & 9.988 & 9.93 & 64.687 & \\ Error between TS and UAV Photogrammetry & 0.013 & 0.036 & 0.060 & 0.020 & \(-0.006\) & \(-0.190\) & 0.024 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Distance errors between the LiDAR and UAV photogrammetry Surveys. It is important to note that this investigation's most significant advantage was the ability to customize the system according to our specifications and the equipment available. Furthermore, as other projects have shown, developing our system allows considering factors like improvement and change of the supporting structure as new technology emerges [10; 19], defining the location of the sensor according to the user needs [10; 19] and using low-cost equipment [1].