Abstract

Laser point cloud registration is a key step in multisource laser scanning data fusion and application. Aimed at the problems of fewer overlapping regional features and the influence of building eaves on registration accuracy, a hierarchical registration algorithm of laser point clouds that considers building eave attributes is proposed in this paper. After extracting the building feature points of airborne and vehicle-borne light detection and ranging data, the similarity measurement model is constructed to carry out coarse registration based on pseudo-conjugate points. To obtain the feature points of the potential eaves (FPPE), the building contour lines of the vehicle-borne data are extended using the direction prediction algorithm. The FPPE data are regarded as the search set, in which the iterative closest point (ICP) algorithm is employed to match the true conjugate points between the airborne laser scanning data and vehicle-borne laser scanning data. The ICP algorithm is used again to complete the fine registration. To evaluate the registration performance, the developed method was applied to the data processing near Shandong University of Science and Technology, Qingdao, China. The experimental results showed that the FPPE dataset can effectively address the coarse registration accuracy effects on the convergence of the iterative ICP. Before considering eave attributes, the mean registration errors (MREs) of the proposed method in the $xoz$ plane, $yoz$ plane, and $xoy$ plane are 0.318, 0.96, and 0.786 m, respectively. After considering eave attributes, the MREs decrease to 0.129, 0.187, and 0.169 m, respectively. The developed method can effectively improve the registration accuracy of the laser point clouds, which not only solves the problem of matching true conjugate points under the effects of the eaves but also avoids converging to a local minimum due to ICP’s poor coarse registration.

© 2021 Optical Society of America

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