In this article, we study the problem of joint localization and target tracking using a mobile robot network. Here, a team of mobile robots equipped with onboard sensors simultaneously localize themselves and track multiple targets. We introduce a fully distributed algorithm that is applicable to generic robot motion, target process, and measurement models and is robust to time-varying sensing and communication topologies and changing blind robots (the robots not directly sensing the targets). Instead of treating localization and target tracking as two separate problems, we explicitly account for the influence of one to the other and exploit it to improve performance in a fully distributed context. Two novel kinds of distributed estimates are derived. By employing them, each robot can estimate the pose (position and orientation) of itself (localization) and the states of targets (tracking) using only its local information and information from its one-hop communicating neighbors while preserving consistency. Furthermore, it is proven that, in the case of linear time-varying models, the estimation errors are bounded in the mean-square sense under very mild conditions on the sensing and communication graph and system observability. The effectiveness of our approach is demonstrated extensively through Monte Carlo simulations, and experiments carried out using a real-world data set. It is also shown better performance in the pose estimates of the robots is achieved when jointly estimating the robots’ poses and targets’ states.

中文翻译：

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使用移动机器人网络进行完全分布式关节定位和目标跟踪

在本文中，我们使用移动机器人网络研究联合定位和目标跟踪问题。在这里，一组配备了机载传感器的移动机器人同时定位自己并跟踪多个目标。我们引入了一种完全分布式算法，该算法适用于通用机器人运动、目标过程和测量模型，并且对时变传感和通信拓扑以及不断变化的盲人机器人（机器人不直接感知目标）具有鲁棒性。我们没有将定位和目标跟踪视为两个独立的问题，而是明确地考虑了一个对另一个的影响，并利用它来提高完全分布式环境中的性能。导出了两种新颖的分布式估计。通过雇用他们，每个机器人可以仅使用其本地信息和来自其一跳通信邻居的信息来估计自身（定位）的姿态（位置和方向）和目标（跟踪）的状态，同时保持一致性。此外，已经证明，在线性时变模型的情况下，在传感和通信图以及系统可观察性的非常温和的条件下，估计误差在均方意义上是有界的。我们的方法的有效性通过蒙特卡罗模拟和使用真实世界数据集进行的实验得到广泛证明。当联合估计机器人的姿态和目标的状态时，还表现出更好的机器人姿态估计性能。