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.

翻译：协同在线标量场映射是多机器人系统的一项重要任务。高斯过程回归被广泛用于构建具有置信区间的空间信息地图。然而，由于其高计算和通信成本，难以处理协同在线映射任务。本文提出了一种通过分布式稀疏高斯过程回归的资源高效协同在线场映射方法。开发了一种新颖的分布式在线高斯过程评估方法，使机器人能够协同评估并找到具有足够全局效用的观测值，从而减少计算量。理论上保证了分布式聚合结果的有界误差，并通过真实在线光场映射实验验证了所提算法的性能。