Measurement Scheduling for Cooperative Localization in Resource-Constrained Conditions

TL;DR

This paper presents a low-complexity, communication-free measurement scheduling method for cooperative localization in resource-limited mobile robot teams, improving efficiency without full observability assumptions.

Contribution

It introduces a greedy, decentralized scheduling approach that does not require inter-robot communication or full observability, suitable for resource-constrained environments.

Findings

Effective in reducing localization uncertainty in simulations

Operates with polynomial time complexity

No inter-robot communication needed during scheduling

Abstract

This paper studies the measurement scheduling problem for a group of N mobile robots moving on a flat surface that are preforming cooperative localization (CL). We consider a scenario in which due to the limited on-board resources such as battery life and communication bandwidth only a given number of relative measurements per robot are allowed at observation and update stage. Optimal selection of which teammates a robot should take a relative measurement from such that the updated joint localization uncertainty of the team is minimized is an NP-hard problem. In this paper, we propose a suboptimal greedy approach that allows each robot to choose its landmark robots locally in polynomial time. Our method, unlike the known results in the literature, does not assume full-observability of CL algorithm. Moreover, it does not require inter-robot communication at scheduling stage. That is, there is no need for the robots to collaborate to carry out the landmark robot selections. We discuss the application of our method in the context of an state-of-the-art decentralized CL algorithm and demonstrate its effectiveness through numerical simulations. Even though our solution does not come with rigorous performance guarantees, its low computational cost along with no communication requirement makes it an appealing solution for operatins with resource constrained robots.