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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 67-72     DOI: 10.6046/gtzyyg.2018.02.09
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3D city model construction based on a consumer-grade UAV
Zhongdi YU1,2(), Hui LI1(), Fang BA1, Zhaoyang WANG1
1. School of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2. Beijing North-star Digital Remote Sensing Technology Co., Ltd., Beijing 100120, China
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Abstract  

3D modeling of urban buildings is one of the key technologies for smart city construction. The traditional modeling methods have many disadvantages in the process of data collection, such as operating difficulty, high cost and low efficiency in 3D modeling. In this paper, the authors propose a 3D city modeling approach based on oblique photography technology of unmanned aerial vehicle(UAV) for consumption. The cradle is used to control the direction of lens, and multi-angle slanted images are obtained. Then the 3D model is constructed by using aerial triangulation principle, and textures of building walls are extracted from multi-angle images. Finally the texture is mapped to the corresponding models, and the true 3D model is built. Result shows that the approach can not only improve modeling efficiency but also reduce the cost during data producing process.

Keywords consumer-grade UAV      oblique photogrammetry      3D model     
:  P23  
Corresponding Authors: Hui LI     E-mail: 1786399629@qq.com;leelmars@gmail.com
Issue Date: 30 May 2018
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Zhongdi YU
Hui LI
Fang BA
Zhaoyang WANG
Cite this article:   
Zhongdi YU,Hui LI,Fang BA, et al. 3D city model construction based on a consumer-grade UAV[J]. Remote Sensing for Land & Resources, 2018, 30(2): 67-72.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.09     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/67
Fig.1  Phantom 4 UAV and ground control system
Fig.2  Flowchart of three-dimensional model construction
Fig.3  Flight planning
Fig.4  Pictures with multi-view
Fig.5  Result of aerotriangulation
Fig.6  Dense point cloud and DSM
Fig.7  3D model with texture
误差参数 平面 高程
最大值 0.066 0 0.012 0
最小值 0.018 2 -0.000 1
中误差 0.030 0 0.011 0
Tab.1  Statistics for the error of check points(m)
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