This project aims to improve the position estimation of mobile mapping platforms. Mobile Mapping (MM) is a technique to obtain geo-information on a large scale using sensors mounted on a car or another vehicle. Under normal conditions, accurate positioning is provided by the integration of Global Navigation Satellite Systems (GNSS) and Inertial Navigation Systems (INS). However, especially in urban areas, where building structures impede a direct line-of-sight to navigation satellites or lead to multipath effects, MM derived products, such as laser point clouds or images, lack the expected reliability and contain an unknown positioning error. This issue has been addressed by many researchers, whose aim to mitigate these effects mainly concentrates on utilising tertiary data, such as digital maps, ortho images or airborne LiDAR. These data serve as a reliable source of orientation and are being used subsidiarily or as the basis for adjustment. However, these approaches show limitations regarding the achieved accuracy, the correction of error in height, the availability of tertiary data and their feasibility in difficult areas. This project is addressing the aforementioned problem by employing high resolution aerial nadir and oblique imagery as reference data. By exploiting the MM platform?s approximate orientation parameters, very accurate matching techniques can be realised to extract the MM platform?s positioning error. In the form of constraints, they serve as a corrective for an orientation update, which is conducted by an estimation or adjustment technique.