Cooperative Robot Localization Using Event-triggered Estimation

TL;DR

This paper introduces an event-triggered cooperative localization method for robotic teams that reduces communication costs while maintaining high estimation accuracy, using a novel fusion and synchronization strategy.

Contribution

It proposes a new event-based estimation algorithm with covariance intersection for efficient, communication-spare robot localization, including heuristic threshold balancing for large networks.

Findings

Achieves near-optimal estimation with less communication.

Maintains error bounds across large networks.

Robust to lossy communication conditions.

Abstract

This paper describes a novel communication-spare cooperative localization algorithm for a team of mobile unmanned robotic vehicles. Exploiting an event-based estimation paradigm, robots only send measurements to neighbors when the expected innovation for state estimation is high. Since agents know the event-triggering condition for measurements to be sent, the lack of a measurement is thus also informative and fused into state estimates. The robots use a Covariance Intersection (CI) mechanism to occasionally synchronize their local estimates of the full network state. In addition, heuristic balancing dynamics on the robots' CI-triggering thresholds ensure that, in large diameter networks, the local error covariances remains below desired bounds across the network. Simulations on both linear and nonlinear dynamics/measurement models show that the event-triggering approach achieves nearly optimal state estimation performance in a wide range of operating conditions, even when using only a fraction of the communication cost required by conventional full data sharing. The robustness of the proposed approach to lossy communications, as well as the relationship between network topology and CI-based synchronization requirements, are also examined.