This paper considers the problem of distributed cooperative localization (CL) via robot-to-robot measurements for a multi-robot system. We propose a distributed consistent CL algorithm. The key idea is to perform the EKF-based state estimation in a transformed coordinate system. Specifically, a coordinate transformation is constructed by decomposing the state-propagation Jacobian by which the correct observability properties are guaranteed. Moreover, the transformed state-propagation Jacobian becomes an identity matrix which is more suitable for distribution. In the proposed algorithm, a server-based framework is adopted to distributely estimate the robot pose in which each robot propagates its pose estimations and the server maintains the correlations. To reduce communication costs, only when the multi-robot system takes a robot-to-robot relative measurement, the robots and the server exchange information to update the pose estimations and the correlations. In addition, no assumptions are made about the type of robots or relative measurements. The proposed algorithm has been validated by experiments and shown to outperform the state-of-art algorithms in terms of consistency and accuracy.

翻译：本文研究了针对多机器人系统通过机器人间测量实现分布式协同定位（CL）的问题。我们提出了一种分布式一致协同定位算法，其核心思想是在变换坐标系下执行基于扩展卡尔曼滤波（EKF）的状态估计。具体而言，通过分解状态传播雅可比矩阵构造坐标变换，从而保证正确的可观测性特性。此外，变换后的状态传播雅可比矩阵成为更适合分布式实现的单位矩阵。在所提出的算法中，采用基于服务器的框架实现机器人位姿的分布式估计：每个机器人传播其位姿估计值，而服务器维护各估计值之间的相关性。为降低通信成本，仅当多机器人系统进行机器人间相对测量时，机器人与服务器才交换信息以更新位姿估计及相关性。此外，该方法未对机器人类型或相对测量方式做任何假设。通过实验验证，所提算法在一致性和精度方面均优于当前最优算法。