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.
Highlights
Under the global trend of intelligent development, intelligent transportation construction is in full swing
Our key contributions are summarized as follows: we propose a vehicle-borne light detection and ranging (LiDAR) point cloud-assisted aerotriangulation method based on non-rigid registration
RFeimgoutereSe1n.s.O20u1r9,m11e, t1h18o8d mainly includes the following key processes: (1) Aerotriangulation of UAV images: during aerotriangulation, global positioning system (GPS) and inertial measurement unit (IMU) data are employed during the rough absolute orientation to generate a 3D feature point cloud of images in 3th. eMgeetohdoedtoiclocogoyrdinate system, which is roughly aligned to the LiDAR point cloud
Summary
Under the global trend of intelligent development, intelligent transportation construction is in full swing. The acquisition of high-precision road geometry information is the foundation of intelligent transportation construction. The control points need to be manually deployed and measured in order to obtain high-precision results, resulting in a low degree of automation. The accuracy of the uncontrolled mapping technology based on RTK has reached 5 cm, but its point cloud elevation accuracy and density are still lower than those of VLS (Vehicle-borne Laser Scanning) [3].With the heterogeneity of the sensors, how to use the information obtained by multiple sensors to form an intelligent service system and avoid the insufficiency of limited data sources is the current research direction [4]
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