Airborne LiDAR (Light Detection And Ranging) and airborne photogrammetry are both proven and widely used techniques for the 3D topographic mapping of extended areas. Although both techniques are based on different reconstruction principles (polar measurement vs. ray triangulation), they ultimately serve the same purpose, the 3D reconstruction of the Earths surface. It is therefore obvious for many applications to integrate the data from both techniques to generate more accurate and complete results. Many works have been published on this topic of data fusion. However, no integrated solution existed prior to this work for the first steps that need to be carried out after data acquisition, namely (a) the lidar strip adjustment and (b) the aerial triangulation. A consequence of solving these two optimization problems independently can be large discrepancies (of up to several decimeters) between the lidar block and the image block. This is especially the case in challenging situations, e.g. corridor mapping with one strip only or in case few or no ground truth data is available. To avoid this problem and thereby profit from many other advantages, a first rigorous integration of these two tasks, the hybrid orientation of lidar point clouds and aerial images, is presented in this thesis. The main purpose of the presented method is to simultaneously optimize the relative orientation and absolute orientation (georeference) of the lidar and image data. This data can be used afterwards to generate accurate and consistent 3D or 2D mapping products. The orientation of the lidar and image data is optimized by minimizing the discrepancies (a) within the overlap area of this data and (b) with respect to ground truth data, if available. The measurement process is thereby rigorously modelled using the original measurements of the sensors (e.g. the polar measurements of the scanner) and the flight trajectory of the aircraft. This way, systematic measurement errors can be corrected where they originally occur. Both, lidar scanners and cameras, can be fully re-calibrated by estimating their interior calibration and mounting calibration. Systematic measurement errors of the flight trajectory can be corrected individually for each flight strip. For highest accuracy demands, time-dependent errors can be modelled by natural cubic splines. The methodological framework of the hybrid adjustment was adapted from the ICP algorithm. Consequently, correspondences are established iteratively and on a point basis to maintain the highest possible resolution level of the data. Four different strategies are presented for the selection of correspondences within the overlap area of point clouds. Thereby, the Maximum Leverage Sampling strategy is newly introduced. It automatically selects those correspondences that are best suited for the estimation of the transformation parameters. The various aspects of the hybrid adjustment are discussed on the basis of four examples. It is demonstrated, that the integration of the lidar strip adjustment and aerial triangulation leads to many synergetic effects. Two of the major advantages are an increased block stability (avoiding block deformations, e.g. bending) and an improved determinability of the parameters.