Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 297-300, 2014
https://doi.org/10.5194/isprsarchives-XL-4-297-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
23 Apr 2014
Accuracy Evaluation of 3D Geometry from Low-Attitude UAV collections A case at Zijin Mine
Q. Wang1, L. Wu2, S. Chen3, D. Shu4, Z. Xu1, F. Li5, and R. Wang5 1Key Laboratory of Environmental Change & Natural Disaster of MOE, Beijing Normal University, Beijing 100875, China
2IOT Perception Mine Research Centre, China University of Mining and Technology, Xuzhou 221008, China
3Resources Department, Longyan College, Longyan 364000, China
4Zijin MiningGroup Co., Ltd, Shanghang 364200. China
5College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China
Keywords: UAV, 3D Geometry, Opencast Mine, Structure from Motion (SfM), Accuracy Evaluation Abstract. This study investigates the usability of low-attitude unmanned aerial vehicle (UAV) acquiring high resolution images for the geometry reconstruction of opencast mine. Image modelling techniques like Structure from Motion (SfM) and Patch-based Multiview Stereo (PMVS) algorithms are used to generate dense 3D point cloud from UAV collections. Then, precision of 3D point cloud will be first evaluated based on Real-time Kinematic (RTK) ground control points (GCPs) at point level. The experimental result shows that the mean square error of the UAV point cloud is 0.11 m. Digital surface model (DSM) of the study area is generated from UAV point cloud, and compared with that from the Terrestrial Laser Scanner (TLS) data for further comparison at the surface level. Discrepancy map of 3D distances based on DSMs shows that most deviation is less than ±0.4 m and the relative error of the volume is 1.55 %.
Conference paper (PDF, 574 KB)


Citation: Wang, Q., Wu, L., Chen, S., Shu, D., Xu, Z., Li, F., and Wang, R.: Accuracy Evaluation of 3D Geometry from Low-Attitude UAV collections A case at Zijin Mine, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4, 297-300, https://doi.org/10.5194/isprsarchives-XL-4-297-2014, 2014.

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