SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems
This addresses the challenge of reliable multi-robot operations in unknown environments, though it appears incremental as it builds on existing techniques like Gaussian Processes and graph optimization.
The paper tackled the problem of achieving accurate localization and effective exploration simultaneously in multi-robot systems, resulting in a method that outperformed state-of-the-art approaches in simulations.
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.