In this paper, we consider multi-robot localization problems with focus on cooperative localization and observability analysis of relative pose estimation. For cooperative localization, there is extra information available to each robot via communication network and message passing. If odometry data of a target robot can be transmitted to the ego-robot then the observability of their relative pose estimation can be achieved by range-only or bearing-only measurements provided both of their linear velocities are non-zero. If odometry data of a target robot is not directly transmitted but estimated by the ego-robot then there must be both range and bearing measurements to guarantee the observability of relative pose estimation. For ROS/Gazebo simulations, we consider four different sensing and communication structures in which extended Kalman filtering (EKF) and pose graph optimization (PGO) estimation with different robust loss functions (filtering and smoothing with different batch sizes of sliding window) are compared in terms of estimation accuracy. For hardware experiments, two Turtlebot3 equipped with UWB modules are used for real-world inter-robot relative pose estimation, in which both EKF and PGO are applied and compared.

翻译：本文研究了多机器人定位问题，重点聚焦于协同定位以及相对位姿估计的可观性分析。在协同定位中，每个机器人可通过通信网络和消息传递获取额外信息。若目标机器人的里程计数据可传输至自车机器人，则仅需距离测量或仅需方位测量即可实现两者相对位姿估计的可观性，前提是两者的线速度均不为零。若目标机器人的里程计数据未直接传输，而是由自车机器人估计得到，则必须同时具备距离测量和方位测量，才能保证相对位姿估计的可观性。针对ROS/Gazebo仿真环境，我们设计了四种不同的感知与通信结构，比较了扩展卡尔曼滤波与采用不同鲁棒损失函数的位姿图优化估计（包括不同滑动窗口批尺寸下的滤波与平滑）在估计精度方面的表现。在硬件实验中，两辆配备UWB模块的Turtlebot3机器人被用于真实世界的机器人间相对位姿估计，并应用和比较了扩展卡尔曼滤波与位姿图优化两种方法。