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http://dx.doi.org/10.7848/ksgpc.2020.38.3.175

Registration of Three-Dimensional Point Clouds Based on Quaternions Using Linear Features  

Kim, Eui Myoung (Department of Spatial Information Engineering, Namseoul University)
Seo, Hong Deok (Department of Spatial Information Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.38, no.3, 2020 , pp. 175-185 More about this Journal
Abstract
Three-dimensional registration is a process of matching data with or without a coordinate system to a reference coordinate system, which is used in various fields such as the absolute orientation of photogrammetry and data combining for producing precise road maps. Three-dimensional registration is divided into a method using points and a method using linear features. In the case of using points, it is difficult to find the same conjugate point when having different spatial resolutions. On the other hand, the use of linear feature has the advantage that the three-dimensional registration is possible by using not only the case where the spatial resolution is different but also the conjugate linear feature that is not the same starting point and ending point in point cloud type data. In this study, we proposed a method to determine the scale and the three-dimensional translation after determining the three-dimensional rotation angle between two data using quaternion to perform three-dimensional registration using linear features. For the verification of the proposed method, three-dimensional registration was performed using the linear features constructed an indoor and the linear features acquired through the terrestrial mobile mapping system in an outdoor environment. The experimental results showed that the mean square root error was 0.001054m and 0.000936m, respectively, when the scale was fixed and if not fixed, using indoor data. The results of the three-dimensional transformation in the 500m section using outdoor data showed that the mean square root error was 0.09412m when the six linear features were used, and the accuracy for producing precision maps was satisfied. In addition, in the experiment where the number of linear features was changed, it was found that nine linear features were sufficient for high-precision 3D transformation through almost no change in the root mean square error even when nine linear features or more linear features were used.
Keywords
Three-dimensional Transformation; Linear Feature; Quaternion; Terrestrial Mobile Mapping System; Absolute orientation; Precise Road Map;
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Times Cited By KSCI : 9  (Citation Analysis)
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