In this study, we address multi-robot localization issues, with a specific focus on cooperative localization and observability analysis of relative pose estimation. Cooperative localization involves enhancing each robot's information through a communication network and message passing. If odometry data from a target robot can be transmitted to the ego robot, observability of their relative pose estimation can be achieved through range-only or bearing-only measurements, provided both robots have non-zero linear velocities. In cases where odometry data from a target robot are not directly transmitted but estimated by the ego robot, both range and bearing measurements are necessary to ensure observability of relative pose estimation. For ROS/Gazebo simulations, we explore four sensing and communication structures. We compare extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation using different robust loss functions (filtering and smoothing with varying batch sizes of sliding windows) in terms of estimation accuracy. In hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, applying both EKF and PGO and comparing their performance.

翻译：本研究聚焦于多机器人定位问题，特别关注协同定位与相对位姿估计的可观测性分析。协同定位通过通信网络和消息传递增强各机器人信息。当目标机器人的里程计数据可传输至本机器人时，若两机器人均具有非零线速度，则可通过纯距离或纯方位测量实现相对位姿估计的可观测性。当目标机器人里程计数据不直接传输而由本机器人进行估计时，需同时使用距离和方位测量以确保相对位姿估计的可观测性。针对ROS/Gazebo仿真，我们研究了四种感知与通信结构，并采用不同鲁棒损失函数（滑动窗口可变批次的滤波与平滑）比较了扩展卡尔曼滤波（EKF）与位姿图优化（PGO）的估计精度。在硬件实验中，使用搭载UWB模块的两台Turtlebot3进行真实环境机器人间相对位姿估计，分别应用EKF与PGO并对比其性能。