Urban search and rescue robots explore the area which they don't know. They must localize themselves, map the environment, and choose their targets. The usage of robot teams can be very effective for large areas instead of a single robot. Robot teams can be managed with distributed or centric methodologies. In a distributed architecture, each robot should be self-sufficient by means of all search tasks. This also means that each robot needs a rich computational power. Moreover, an optimal exploration strategy requires communication between all the robots. A common way to get such an architecture is applying centric approaches. In centric approaches, each robot can be seen as a mobile sensor with little computational power. They send their measures to the center. The map is generated at the center. Navigation commands are generated at the center according to the exploration strategy. ROS is a very common platform for robotic researchers. It includes several single robot mapping algorithms. But, there is no common mapping algorithm for centric approaches. In this study, we developed a multi-robot version of Hector mapping which is widely used in most robotic researches. For the real-time running ability, we parallelized its optimization procedure. The experimental results shows the effectiveness of our proposed architecture.