This paper shows a method to register point clouds from images of UAV-mounted airborne cameras as well as airborne laser scanner data. The focus is a general technique which does rely neither on linear or planar structures nor on the point cloud density. Therefore, the proposed approach is also suitable for rural areas and water bodies captured via different sensor configurations. This approach is based on a regular 2.5D grid generated from the segmented ground points of the 3D point cloud. It is assumed that initial values for the registration are already estimated, e.g. by measured exterior orientation parameters with the UAV mounted GNSS and IMU. These initial parameters are finely tuned by minimizing the distances between the 3D points of a target point cloud to the generated grid of the source point cloud in an iteration process. To eliminate outliers (e.g., vegetation points) a threshold for the distances is defined dynamically at each iteration step, which filters ground points during the registration. The achieved accuracy of the registration is up to 0.4 m in translation and up to 0.3 degrees in rotation, by using a raster size of the DEM of 2 m. Considering the ground sampling distance of the airborne data which is up to 0.4 m between the scan lines, this result is comparable to the result achieved by an ICP algorithm, but the proposed approach does not rely on point densities and is therefore able to solve registrations where the ICP have difficulties.