SEAL: Simultaneous Exploration and Localization in Multi-Robot Systems

TL;DR

SEAL introduces a novel method for simultaneous exploration and localization in multi-robot systems, leveraging Gaussian Processes and communication graph optimization to improve accuracy without relying on global localization.

Contribution

The paper presents a new integrated approach combining Gaussian Process-based information fusion and graph optimization for enhanced multi-robot exploration and localization.

Findings

SEAL outperforms existing methods in exploration efficiency.

SEAL achieves higher localization accuracy in simulations.

The approach is practical for real-world multi-robot applications.

Abstract

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.