Server assisted distributed cooperative localization over unreliable communication links

TL;DR

This paper introduces a server-assisted distributed cooperative localization algorithm for networked robots that is robust to unreliable communication links, offering low computational costs and formal guarantees of estimate optimality.

Contribution

A novel recursive algorithm enabling robots to localize themselves with minimal computation and communication, robust to communication failures, and equivalent to EKF under perfect links.

Findings

Algorithm performs well in simulations and experiments.

Robust to communication failures with formal variance guarantees.

Computational complexity per robot is O(1).

Abstract

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.