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.

本文研究了多机器人定位问题，重点关注合作定位和相对位姿估计的可观测性分析。通过通信网络和消息传递，可以为每个机器人提供额外信息来进行合作定位。如果目标机器人的里程数据可以传输给自我机器人，那么只需要范围测量或方向测量，且两者的线速度均非零，就可以实现相对位姿估计的可观测性。如果目标机器人的里程数据不会直接传输给自我机器人，而是由自我机器人估计，则需要同时进行范围和方向测量，以保证相对位姿估计的可观测性。本文通过 ROS/Gazebo 模拟，比较了四种不同的传感和通信结构，在估计精度方面，使用了扩展卡尔曼滤波 (EKF) 和姿态图优化 (PGO) 估计以及不同的鲁棒损失函数 (使用不同滑动窗口批量大小的滤波和平滑)。在硬件实验中，使用两个装备有 UWB 模块的 Turtlebot3 进行现实世界中的机器人间相对位姿估计，并进行了 EKF 和 PGO 的应用和比较。

SE (2) 中的多机器人相对位姿估计与可观测性分析：扩展卡尔曼滤波和鲁棒位姿图优化的比较

Multi-Robot Relative Pose Estimation in SE(2) with Observability  Analysis: A Comparison of Extended Kalman Filtering and Robust Pose Graph  Optimization