  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.
