  We propose an autonomous exploration algorithm designed for decentralized
multi-robot teams, which takes into account map and localization uncertainties
of range-sensing mobile robots. Virtual landmarks are used to quantify the
combined impact of process noise and sensor noise on map uncertainty.
Additionally, we employ an iterative expectation-maximization inspired
algorithm to assess the potential outcomes of both a local robot's and its
neighbors' next-step actions. To evaluate the effectiveness of our framework,
we conduct a comparative analysis with state-of-the-art algorithms. The results
of our experiments show the proposed algorithm's capacity to strike a balance
between curbing map uncertainty and achieving efficient task allocation among
robots.
