Citation: Gao, S.; Gan, S.; Yuan, X.;
Bi, R.; Li, R.; Hu, L.; Luo, W.
Experimental Study on 3D
Measurement Accuracy Detection of
Low Altitude UAV for Repeated
Observation of an Invariant Surface.
Processes 2022, 10, 4. https://doi.org/
10.3390/pr10010004
Academic Editors: Arkadiusz Gola,
Izabela Nielsen and Patrik Grznár
Received: 22 October 2021
Accepted: 15 December 2021
Published: 21 December 2021
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Article
Experimental Study on 3D Measurement Accuracy Detection of
Low Altitude UAV for Repeated Observation of an
Invariant Surface
Sha Gao
1
, Shu Gan
1,
*, Xiping Yuan
2,3
, Rui Bi
1
, Raobo Li
1
, Lin Hu
1
and Weidong Luo
1
1
School of Land and Resources Engineering, Kunming University of Science and Technology,
Kunming 650093, China; kmust_gs@126.com (S.G.); biruikmust@163.com (R.B.); 18469110089@163.com (R.L.);
hl112365@163.com (L.H.); gtzygc311@163.com (W.L.)
2
Plication Engineering Research Center, Spatial Information Surveying and Mapping Technology in Plateau
and Mountainous Areas Set by Universities in Yunnan Province, Kunming 650093, China; gskmust@163.com
3
Key Laboratory of Mountain Real Scene Point Cloud Data Processing and Application for Universities in West
Yunnan University of Applied Sciences, Dali 671006, China
* Correspondence: gs@kust.edu.cn; Tel.:+86-135-7703-2539
Abstract:
Low-altitude unmanned aerial vehicle (UAV) photogrammetry combined with structure-
from-motion (SFM) algorithms is the latest technological approach to imaging 3D stereo constructions.
At present, derivative products have been widely used in landslide monitoring, landscape evolution,
glacier movement, volume measurement, and landscape change detection. However, there is still
a lack of research into the accuracy of 3D data positioning based on the structure-from-motion of
unmanned aerial vehicle (UAV-SFM) technology, itself, which can affect the measurable effectiveness
of the results in further applications of this technological approach. In this paper, validation work
was carried out for the DJI Phantom 4 RTK UAV, for earth observation data related to 3D positioning
accuracy. First, a test plot with a relatively stable surface was selected for repeated flight imaging
observations. Specifically, three repeated flights were performed on the test plot to obtain three
sorties of images; the structure from motion and multi-view stereo (SFM-MVS) key technology was
used to process and construct a 3D scene model, and based on this model the digital surface model
(DSM) and digital orthophoto map (DOM) data of the same plot with repeated observations were
obtained. In order to check the level of 3D measurement accuracy of the UAV technology itself,
a window selection-based method was used to sample the point cloud set data from the three-sortie
repeat observation 3D model. The DSM and DOM data obtained from three repeated flights over the
surface invariant test plots were used to calculate the repeat observation 3D point errors, taking into
account the general methodology of redundant observation error analysis for topographic surveys.
At the same time, to further analyze the limits of the UAV measurement technique, possible under
equivalent observation conditions with the same processing environment, a difference model (DOD)
was constructed for the DSM data from three sorties, to deepen the overall characterization of the
differences between the DSMs obtained from repeated observations. The results of the experimental
study concluded that both the analysis of the 3D point set measurements based on window sampling
and the accuracy evaluation using the difference model were generally able to achieve a centimeter
level of planimetric accuracy and vertical accuracy. In addition, the accuracy of the surface-stabilized
hardened ground was better, overall, than the accuracy of the non-hardened ground. The results of
this paper not only probe the measurement limits of this type of UAV, but also provide a quantitative
reference for the accurate control and setting of an acquisition scheme of the UAV-based SfM-MVS
method for geomorphological data acquisition and 3D reconstruction.
Keywords: UAV; SFM-MVS; DSM; DoD; point error; precision analysis
Processes 2022, 10, 4. https://doi.org/10.3390/pr10010004 https://www.mdpi.com/journal/processes
