The availability of accurate localization is critical for multi-robot
exploration strategies; noisy or inconsistent localization causes failure in
meeting exploration objectives. We aim to achieve high localization accuracy
with contemporary exploration map belief and vice versa without needing global
localization information. This paper proposes a novel simultaneous exploration
and localization (SEAL) approach, which uses Gaussian Processes (GP)-based
information fusion for maximum exploration while performing communication graph
optimization for relative localization. Both these cross-dependent objectives
were integrated through the Rao-Blackwellization technique. Distributed
linearized convex hull optimization is used to select the next-best unexplored
region for distributed exploration. SEAL outperformed cutting-edge methods on
exploration and localization performance in extensive ROS-Gazebo simulations,
illustrating the practicality of the approach in real-world applications.