  With the deepening of research on the SLAM system, the possibility of
cooperative SLAM with multi-robots has been proposed. This paper presents a map
matching and localization approach considering the cooperative SLAM of an
aerial-ground system. The proposed approach aims to help precisely matching the
map constructed by two independent systems that have large scale variance of
viewpoints of the same route and eventually enables the ground mobile robot to
localize itself in the global map given by the drone. It contains dense mapping
with Elevation Map and software "Metashape", map matching with a proposed
template matching algorithm, weighted normalized cross-correlation (WNCC) and
localization with particle filter. The approach enables map matching for
cooperative SLAM with the feasibility of multiple scene sensors, varies from
stereo cameras to lidars, and is insensitive to the synchronization of the two
systems. We demonstrate the accuracy, robustness, and the speed of the approach
under experiments of the Aero-Ground Dataset.
