(c) 
Figure 7. Overview of the image block: (a) Galaxy S, (b) S2 
type A, and (c) S2 type B. 
The image triangulation was conducted in accordance with 
presence or absence of consideration of the interior orientation 
(IO) parameters determined by camera calibration. In case IO 
parameters were taken into account in the static experiment, the 
results from triangulation for any smartphone type were within 
1.5 pixel (RMSE), which was improved at least by 35% 
compared to when IO parameters were not taken into account. 
On the contrary, the improvement effect of considering IO 
parameters on accuracy in triangulation for smartphone images 
in dynamic experiment was not significant compared to the 
static experiment. It was due to the significant impact of 
vibration and sudden attitude change of UAV on the actuator 
for automatic focus control within the camera built in 
smartphone under the dynamic condition. 
4. CONCLUSIONS 
This paper introduced a new photogrammetric UAV system 
using the smartphone technology. Although these results 
showed slightly lower accuracy than the results using existing 
system consisting of expensive sensors, they seems to be 
satisfactory in terms of cost and efficiency in that they were 
generated by only one smartphone. And this paper assessed the 
feasibility of smartphone as a payload for photogrammetric 
UAV system. Considering that these results were obtained from 
only 1 smartphone, they suggest that smartphone is not only 
very feasible as a payload for UAV system but also play direct 
or indirect roles loaded to UAV system. In addition, it is 
expected that the performances of MEMS sensors built in 
smartphone e will develop continuously, which in turn will 
increase the use of smartphone as a payload for 
photogrammetric UAV system in the future. 
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6. ACKNOWLEDGEMENTS 
t to educate 
This work was researched by the supporting projec 
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