Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

TL;DR

This paper introduces a neural network-based surrogate model to efficiently schedule measurements in cooperative localization, reducing communication, computation, and memory costs while maintaining performance.

Contribution

It proposes a novel surrogate model for the sequential greedy measurement scheduling algorithm that operates with local information and minimal communication.

Findings

Reduces inter-agent communication message size.

Decreases computational complexity of merit function evaluations.

Maintains localization performance with lower resource usage.

Abstract

In cooperative localization, communicating mobile agents use inter-agent relative measurements to improve their dead-reckoning-based global localization. Measurement scheduling enables an agent to decide which subset of available inter-agent relative measurements it should process when its computational resources are limited. Optimal measurement scheduling is an NP-hard combinatorial optimization problem. The so-called sequential greedy (SG) algorithm is a popular suboptimal polynomial-time solution for this problem. However, the merit function evaluation for the SG algorithms requires access to the state estimate vector and error covariance matrix of all the landmark agents (teammates that an agent can take measurements from). This paper proposes a measurement scheduling for CL that follows the SG approach but reduces the communication and computation cost by using a neural network-based surrogate model as a proxy for the SG algorithm's merit function. The significance of this model is that it is driven by local information and only a scalar metadata from the landmark agents. This solution addresses the time and memory complexity issues of running the SG algorithm in three ways: (a) reducing the inter-agent communication message size, (b) decreasing the complexity of function evaluations by using a simpler surrogate (proxy) function, (c) reducing the required memory size.Simulations demonstrate our results.