  Multi-robot exploration is a field which tackles the challenge of exploring a
previously unknown environment with a number of robots. This is especially
relevant for search and rescue operations where time is essential. Current
state of the art approaches are able to explore a given environment with a
large number of robots by assigning them to frontiers. However, this assignment
generally favors large frontiers and hence omits potentially valuable
medium-sized frontiers.
  In this paper we showcase a novel multi-robot exploration algorithm, which
improves and adapts the existing approaches. Through the addition of
information gain based ranking we improve the exploration time for closed urban
environments while maintaining similar exploration performance compared to the
state-of-the-art for open environments. Accompanying this paper, we further
publish our research code in order to lower the barrier to entry for further
multi-robot exploration research. We evaluate the performance in three
simulated scenarios, two urban and one open scenario, where our algorithm
outperforms the state of the art by 5% overall.
