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，但由于其高昂的计算与通信成本，难以处理协同在线映射任务。本文提出一种基于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。通过开发新型分布式在线高斯过程评估机制，机器人可协同评估并筛选出具有充足全局效用的观测值以降低计算量。理论证明了分布式聚合结果的有界误差，真实在线光场映射实验验证了所提算法的性能。