Co-registration of aerial photogrammetric and LiDAR point clouds in urban environments using automatic plane correspondence

摘要

The co-registration process between light detection and ranging point clouds and photogrammetric digital surface models data is analyzed and a semi-automated solution is implemented. For a robust 3D geometric transformation between the two datasets in an urban environment, both planes and points are used. Initially, planes are chosen as the co-registration primitives. A region-growing algorithm based on a triangulated irregular network is implemented to extract planes from both datasets. Next, an automatic process for identifying and matching corresponding planes from the two datasets has been developed and implemented. The extracted planes are associated as plane pairs, initially by a matching process for buildings, followed by the plane matching algorithm within the building spatial window. Then two different geometric registration algorithms are used to obtain accurate transformation parameters between the two planar datasets. The 3D conformal transformation is applied to obtain the transformation parameters using the corresponding plane pairs. Following the mapping of one dataset into the coordinate system of the other, the mapped original point clouds are also used as another registration feature to complement the plane-based co-registration. In this latter step, the iterative closest point algorithm is applied, using the corresponding building point clouds to further refine the transformation solution. Experimental results together with their assessments are presented and discussed demonstrating the applicability of the proposed approach.