One of the main problems of today's Airborne Laser Scanning (ALS) systems is the lack of reliable data QA/QC (Quality Assurance/ Quality Control) within or shortly after the airborne survey campaign. This thesis presents the development of methods to perform automated processing and QA/QC of ALS data during data acquisition (in-flight). The backbone of these methods is an error propagation algorithm that estimates the expected accuracy of the point-cloud. The error propagation considers the uncertainties induced by the direct georeferencing (DG) and the changing scanning geometry. A novel methodology that derives the scanning geometry directly from the point-cloud and computes a final quality indicator for every laser point is developed. To predict the accuracy of the navigation solution, this research also proposes a methodology to estimate in realtime (RT) the likelihood of fixing the differential carrier-phase ambiguities during post-processing. Furthermore, an innovative procedure to describe the quality of digital terrain models (DTM) derived from ALS data is presented. The successful implementation of these concepts into a fully functional in-flight quality monitoring tool embedded in an ALS system is demonstrated. The proposed tool incorporates RT GPS/INS processing and point-cloud georeferencing. The general performance of the tool, the validity of the quality indicators and the achievable improvement in efficiency for ALS data acquisition are assessed in airborne surveys. It is shown that when using RTK GPS (real-time kinematics) as positioning mode, the tool can provide point-clouds with sub-decimeter accuracy in RT.