  The core problem of visual multi-robot simultaneous localization and mapping
(MR-SLAM) is how to efficiently and accurately perform multi-robot global
localization (MR-GL). The difficulties are two-fold. The first is the
difficulty of global localization for significant viewpoint difference.
Appearance-based localization methods tend to fail under large viewpoint
changes. Recently, semantic graphs have been utilized to overcome the viewpoint
variation problem. However, the methods are highly time-consuming, especially
in large-scale environments. This leads to the second difficulty, which is how
to perform real-time global localization. In this paper, we propose a semantic
histogram-based graph matching method that is robust to viewpoint variation and
can achieve real-time global localization. Based on that, we develop a system
that can accurately and efficiently perform MR-GL for both homogeneous and
heterogeneous robots. The experimental results show that our approach is about
30 times faster than Random Walk based semantic descriptors. Moreover, it
achieves an accuracy of 95% for global localization, while the accuracy of the
state-of-the-art method is 85%.
