Abstract
Light Detection and Ranging (LiDAR) is an active sensor that can effectively acquire a large number of three-dimensional (3-D) points. LiDAR systems can be equipped on different platforms for different applications, but to integrate the data, point cloud registration is needed to improve geometric consistency. The registration of airborne and terrestrial mobile LiDAR is a challenging task because the point densities and scanning directions differ. We proposed a scheme for the registration of airborne and terrestrial mobile LiDAR using the least squares 3-D surface registration technique to minimize the surfaces between two datasets. To analyze the effect of point density in registration, the simulation data simulated different conditions and estimated the theoretical errors. The test data were the point clouds of the airborne LiDAR system (ALS) and the mobile LiDAR system (MLS), which were acquired by Optech ALTM 3070 and Lynx, respectively. The resulting simulation analysis indicated that the accuracy of registration improved as the density increased. For the test dataset, the registration error of mobile LiDAR between different trajectories improved from 40 cm to 4 cm, and the registration error between ALS and MLS improved from 84 cm to 4 cm. These results indicate that the proposed methods can obtain 5 cm accuracy between ALS and MLS.
Highlights
Light detection and ranging (LiDAR) systems are currently common tools to acquire three-dimensional (3-D) surface information
To summarize the process of planar object extraction, the extraction of 3D surface features from irregular points include the following steps: (1) generating voxel structure for irregular points; (2) removing voxels that contain less than 5 LiDAR points; (3) calculating eigenvalues from points inside the voxels; and (4) extracting planar object based on parameter λk.The extracted planes could be located on walls, roofs, and road surfaces in any direction
This study proposed a scheme to co-register the 3-D point clouds scanned from airborne and terrestrial vehicle platforms to increase the details of urban scenes
Summary
Light detection and ranging (LiDAR) systems are currently common tools to acquire three-dimensional (3-D) surface information. The aim of MLS registration is to register the LiDAR points from different trajectories; to obtain larger street sections, MLS usually acquires data from direct and reverse lanes using a scanning mechanism similar to ALS. Boulaassal et al [17] extracted the 3-D vectors of buildings from ALS, TLS, and MLS separately and registered all the extracted 3-D vectors by linear feature for a detailed 3-D building model; they combined the vector data rather than point clouds. The objective of this study was to co-register the point clouds acquired by airborne LiDAR and terrestrial mobile LiDAR and use these complementary data to improve the point coverage in urban areas. The terrestrial mobile LiDAR is transformed to the coordinate system of airborne LiDAR to improve the accuracy of mobile LiDAR in urban corridors
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