  This paper presents a novel approach to build consistent 3D maps for multi
robot cooperation in USAR environments. The sensor streams from unmanned aerial
vehicles (UAVs) and ground robots (UGV) are fused in one consistent map. The
UAV camera data are used to generate 3D point clouds that are fused with the 3D
point clouds generated by a rolling 2D laser scanner at the UGV. The
registration method is based on the matching of corresponding planar segments
that are extracted from the point clouds. Based on the registration, an
approach for a globally optimized localization is presented. Apart from the
structural information of the point clouds, it is important to mention that no
further information is required for the localization. Two examples show the
performance of the overall registration.
