不可靠通信链路上服务器辅助的分布式协作定位
Server assisted distributed cooperative localization over unreliable communication links
摘要：
研究了一组具有有限车载资源的网络化机器人的协作定位问题。我们提出了一种新的递归算法，其中每个机器人本地化本身在一个全球坐标系中的本地航位推算，并机会主义地纠正其姿态估计，每当它收到一个相对的测量更新消息从服务器。每个机器人的计算和存储成本在团队的大小是O（1）的顺序，和机器人只需要传输信息时，他们参与了相对测量。当服务器接收到机器人间测量时，它也只需要计算和发送更新消息。我们表明，在完美的沟通，我们的算法是一个替代的，但确切的实现联合CL整个团队通过扩展卡尔曼滤波器（EKF）。然而，完美的沟通并不是一个硬性要求。事实上，我们表明，我们的算法本质上是强大的通信故障方面，正式的保证，收到更新消息的机器人的更新估计是最小方差在一阶近似意义上，在给定的时间步长。我们证明了该算法的性能在模拟和实验。
英文摘要：
This paper considers the problem of cooperative localization (CL) using inter-robot measurements for a group of networked robots with limited on-board resources. We propose a novel recursive algorithm in which each robot localizes itself in a global coordinate frame by local dead reckoning, and opportunistically corrects its pose estimate whenever it receives a relative measurement update message from a server. The computation and storage cost per robot in terms of the size of the team is of order O(1), and the robots are only required to transmit information when they are involved in a relative measurement. The server also only needs to compute and transmit update messages when it receives an inter-robot measurement. We show that under perfect communication, our algorithm is an alternative but exact implementation of a joint CL for the entire team via Extended Kalman Filter (EKF). The perfect communication however is not a hard requirement. In fact, we show that our algorithm is intrinsically robust with respect to communication failures, with formal guarantees that the updated estimates of the robots receiving the update message are of minimum variance in a first-order approximate sense at that given timestep. We demonstrate the performance of the algorithm in simulation and experiments.
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