Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization

• URL: http://arxiv.org/abs/2112.02843v1
• Date: Mon, 6 Dec 2021 08:06:29 GMT
• Title: Learning-based Measurement Scheduling for Loosely-Coupled Cooperative Localization
• Authors: Jianan Zhu and Solmaz S. Kia
• Abstract summary: 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. This paper proposes a measurement scheduling for CL that follows the sequential computation approach but reduces the communication and cost by using a neural network-based surrogate model as a proxy for the SG's merit function.
• Score: 3.616948583169635
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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