Resource-Efficient Cooperative Online Scalar Field Mapping via Distributed Sparse Gaussian Process Regression

TL;DR

This paper introduces a resource-efficient distributed Gaussian process regression method for cooperative online scalar field mapping in multi-robot systems, reducing computational and communication costs while maintaining accuracy.

Contribution

It presents a novel distributed online Gaussian process evaluation technique that minimizes resource use and guarantees bounded errors in cooperative mapping.

Findings

Effective reduction in computation and communication costs.

Theoretical guarantees of bounded errors.

Validated performance through real light field mapping experiments.

Abstract

Cooperative online scalar field mapping is an important task for multi-robot systems. Gaussian process regression is widely used to construct a map that represents spatial information with confidence intervals. However, it is difficult to handle cooperative online mapping tasks because of its high computation and communication costs. This letter proposes a resource-efficient cooperative online field mapping method via distributed sparse Gaussian process regression. A novel distributed online Gaussian process evaluation method is developed such that robots can cooperatively evaluate and find observations of sufficient global utility to reduce computation. The bounded errors of distributed aggregation results are guaranteed theoretically, and the performances of the proposed algorithms are validated by real online light field mapping experiments.