Automatische änderungsdetektion beim vorwärtsblickenden airborne laserscanning urbaner gebiete

The use of helicopters or UAVs (unmanned aerial vehicles) as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements can be used for terrain-referenced navigation and change detection. In this paper, we summarize a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. The acquisition of reference data with an oblique forward-looking laser scanner is combined with an object-based data analysis, a coregistration of overlapping point clouds, and the boresight calibration of the ALS system. In contrast to classical difference methods, our approach to automatic change detection extends the concept of occupancy grids known from robot mapping. In this approach, sections of 3D space are labeled empty, occupied or unknown with the intention to identify conflicting information along the laser pulse propagation path. To identify these conflicts, we apply the Dempster-Shafer theory of evidence for the representation and fusion of knowledge and the management of uncertainty. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects.