Abstract
Automatic registration of terrestrial laser scanning point clouds is a crucial but unresolved topic that is of great interest in many domains. This study combines terrestrial laser scanner with a smartphone for the coarse registration of leveled point clouds with small roll and pitch angles and height differences, which is a novel sensor combination mode for terrestrial laser scanning. The approximate distance between two neighboring scan positions is firstly calculated with smartphone GPS coordinates. Then, 2D distribution entropy is used to measure the distribution coherence between the two scans and search for the optimal initial transformation parameters. To this end, we propose a method called Iterative Minimum Entropy (IME) to correct initial transformation parameters based on two criteria: the difference between the average and minimum entropy and the deviation from the minimum entropy to the expected entropy. Finally, the presented method is evaluated using two data sets that contain tens of millions of points from panoramic and non-panoramic, vegetation-dominated and building-dominated cases and can achieve high accuracy and efficiency.
Highlights
The applications of terrestrial laser scanning (TLS) are continuously growing in areas such as city modeling, heritage documentation, manufacturing, and terrain surveying
As the external sensors are quite helpful for point registration, this paper presents a novel method for the automatic coarse registration of leveled point clouds, combining terrestrial laser scanner with the smartphone, which is low-cost compared with professional sensors
Because Iterative Minimum Entropy (IME) is a method for coarse registration, the result of IME is processed with the Iterative Closest Point (ICP) algorithm to achieve a complete registration
Summary
The applications of terrestrial laser scanning (TLS) are continuously growing in areas such as city modeling, heritage documentation, manufacturing, and terrain surveying. Weinmann et al [17] extracted characteristic feature points from reflectance images based on SIFT features and projected them into 3D space to calculate transformation parameters This algorithm can achieve a high accuracy without fine registration by using 3D-to-3D geometric constraints. As the external sensors are quite helpful for point registration, this paper presents a novel method for the automatic coarse registration of leveled point clouds, combining terrestrial laser scanner with the smartphone, which is low-cost compared with professional sensors. Combining the terrestrial laser scanner with smartphone for coarse registration; using 2D projection entropy to measure the distribution coherence between two scans; and presenting the Iterative Minimum Entropy (IME) algorithm to correct initial transformation parameters and reduce the effect of positioning error from the smartphone GPS
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