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.

本文提出了一种名为 SEAL 的方法，该方法使用基于高斯过程的信息融合实现最大化探索，同时进行通信图优化以实现相对定位，通过 Rao-Blackwellization 技术将这两个交叉依赖的目标进行整合，并使用分布式线性化凸壳优化来选择下一个最佳未开发的区域。在广泛的 ROS-Gazebo 模拟中，SEAL 在探索和定位性能上优于先进方法，说明了该方法在实际应用中的实用性。