Virtual Image Generation from Low Altitude Unmanned Aerial Vehicle Digital Aerial Camera

2009 ◽  
Author(s):  
Cui Hongxia ◽  
Lin Zongjian ◽  
Meng Wenli
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Image Generation ◽  
Virtual Image ◽  
Aerial Vehicle ◽  
Low Altitude

Development of the navigation complex structure for a high-precision unmanned aerial vehicle for flight in the atmosphere

2020 ◽  
Keyword(s):  
Mathematical Modeling ◽  
Unmanned Aerial Vehicle ◽  
High Precision ◽  
Kalman Filters ◽  
Inertial Navigation System ◽  
Complex Structure ◽  
Navigation Systems ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
System Errors

The navigation systems as part of the navigation complex of a high-precision unmanned aerial vehicle in conditions of different altitude flight are investigated. The working contours of the navigation complex with correction algorithms for an unmanned aerial vehicle during high-altitude and low-altitude flights are formed. Mathematical models of inertial navigation system errors used in non-linear and linear Kalman filters are presented. The results of mathematical modeling demonstrate the effectiveness of the working contours effectiveness of the navigation complex with correction algorithms. Keywords high-precision unmanned aerial vehicle; navigation complex; multi-altitude flight; work circuit; passive noises; Kalman filter; correction

Extraction of Traffic Parameters at Urban Intersections Using the Low-Altitude Unmanned Aerial Vehicle Image

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Can Yang ◽  
Junjie Zhang ◽  
Hongbo Li ◽  
Haiyang Yu ◽  
Yongzheng Xu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle ◽  
Low Altitude

scholarly journals Experimental Study on 3D Measurement Accuracy Detection of Low Altitude UAV for Repeated Observation of an Invariant Surface

Processes ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 4
Author(s):  
Sha Gao ◽  
Shu Gan ◽  
Xiping Yuan ◽  
Rui Bi ◽  
Raobo Li ◽  
...  
Keyword(s):  
Experimental Study ◽  
Unmanned Aerial Vehicle ◽  
Structure From Motion ◽  
Measurement Accuracy ◽  
Test Plot ◽  
3D Measurement ◽  
Difference Model ◽  
Repeated Observations ◽  
Aerial Vehicle ◽  
Low Altitude

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.

scholarly journals An overview of various kinds of wind effects on unmanned aerial vehicle

2019 ◽  
Vol 52 (7-8) ◽  
pp. 731-739 ◽  
Author(s):  
Bo Hang Wang ◽  
Dao Bo Wang ◽  
Zain Anwar Ali ◽  
Bai Ting Ting ◽  
Hao Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Great Influence ◽  
Wind Disturbance ◽  
Wind Fields ◽  
Wind Model ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
The Stability ◽  
The Mathematical Model ◽  
Vehicle Movement

Attitude, speed, and position of unmanned aerial vehicles are susceptible to wind disturbance. The types, characteristics, and mathematical models of the wind, which have great influence on unmanned aerial vehicle in the low-altitude environment, are summarized, including the constant wind, turbulent flow, many kinds of wind shear, and the propeller vortex. Combined with the mathematical model of the unmanned aerial vehicle, the mechanism of unmanned aerial vehicle movement in the wind field is illustrated from three different kinds of viewpoints including velocity viewpoint, force viewpoint, and energy viewpoint. Some simulation tests have been implemented to show the effects of different kinds of wind on unmanned aerial vehicle’s path and flight states. Finally, some proposals are presented to tell reader in which condition, which wind model should be added to simulation, and how to enhance the stability of unmanned aerial vehicle for different kinds of wind fields.

scholarly journals Recent Research Progress of Unmanned Aerial Vehicle Regulation Policies and Technologies in Urban Low Altitude

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 74175-74194
Author(s):  
Chenchen Xu ◽  
Xiaohan Liao ◽  
Junming Tan ◽  
Huping Ye ◽  
Haiying Lu
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Research Progress ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Recent Research Progress

Measuring low-altitude wind gusts using the unmanned aerial vehicle GustAV

2018 ◽  
Vol 6 (4) ◽  
pp. 235-248 ◽  
Author(s):  
Alton Yeung ◽  
Goetz Bramesfeld ◽  
Joon Chung ◽  
Stephen Foster
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Flight Test ◽  
Test System ◽  
Wind Gusts ◽  
Von Karman ◽  
Specific Objective ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
And Performance ◽  
Von Kármán

A small unmanned aerial vehicle (SUAV) was developed with the specific objective to explore atmospheric wind gusts at low altitudes below 500 m. These gusts have significant impact on the flight characteristics and performance of SUAVs. The SUAV carried an advanced air-data system that includes a five-hole probe, which was adapted for this specific application. In several flight tests the entire test system was qualified and gust data were recorded. The subsequent experimentally derived gust data were post-processed and compared with turbulence spectra of the MIL-HDBK-1797 von Kármán turbulence model. On the day of the flight test, the experimental results did not fully match the prediction of the von Kármán model. Meanwhile, the wind measuring apparatus were proven to be able to measure gust during flight. Therefore, a broader sampling will be required to generalize the gust measurements and be compared with the existing models.

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