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Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
2
Anhui Huadian Engineering Consultating&Design CO., LTD, Anhui 230022, China
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
4
Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030-4444, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(10), 1188; https://doi.org/10.3390/rs11101188
Received: 2 April 2019 / Revised: 13 May 2019 / Accepted: 15 May 2019 / Published: 18 May 2019
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
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Abstract

VLS (Vehicle-borne Laser Scanning) can easily scan the road surface in the close range with high density. UAV (Unmanned Aerial Vehicle) can capture a wider range of ground images. Due to the complementary features of platforms of VLS and UAV, combining the two methods becomes a more effective method of data acquisition. In this paper, a non-rigid method for the aerotriangulation of UAV images assisted by a vehicle-borne light detection and ranging (LiDAR) point cloud is proposed, which greatly reduces the number of control points and improves the automation. We convert the LiDAR point cloud-assisted aerotriangulation into a registration problem between two point clouds, which does not require complicated feature extraction and match between point cloud and images. Compared with the iterative closest point (ICP) algorithm, this method can address the non-rigid image distortion with a more rigorous adjustment model and a higher accuracy of aerotriangulation. The experimental results show that the constraint of the LiDAR point cloud ensures the high accuracy of the aerotriangulation, even in the absence of control points. The root-mean-square error (RMSE) of the checkpoints on the x, y, and z axes are 0.118 m, 0.163 m, and 0.084m, respectively, which verifies the reliability of the proposed method. As a necessary condition for joint mapping, the research based on VLS and UAV images in uncontrolled circumstances will greatly improve the efficiency of joint mapping and reduce its cost. View Full-Text
Keywords: vehicle-borne laser point cloud; UAV images; aerial triangulation; non-rigid methods; point cloud registration vehicle-borne laser point cloud; UAV images; aerial triangulation; non-rigid methods; point cloud registration
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zheng, L.; Li, Y.; Sun, M.; Ji, Z.; Yu, M.; Shu, Q. Non-Rigid Vehicle-Borne LiDAR-Assisted Aerotriangulation. Remote Sens. 2019, 11, 1188.

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