A big challenge in environmental monitoring is the spatiotemporal variation
of the phenomena to be observed. To enable persistent sensing and estimation in
such a setting, it is beneficial to have a time-varying underlying
environmental model. Here we present a planning and learning method that
enables an autonomous marine vehicle to perform persistent ocean monitoring
tasks by learning and refining an environmental model. To alleviate the
computational bottleneck caused by large-scale data accumulated, we propose a
framework that iterates between a planning component aimed at collecting the
most information-rich data, and a sparse Gaussian Process learning component
where the environmental model and hyperparameters are learned online by taking
advantage of only a subset of data that provides the greatest contribution. Our
simulations with ground-truth ocean data shows that the proposed method is both
accurate and efficient.

针对环境监测中的时空变异性，我们提出了一种规划与学习方法，通过学习和完善环境模型，实现自主海洋监测任务；该方法结合了最信息丰富数据的规划和稀疏高斯过程学习组件，提高了计算效率，仿真结果表明该方法准确性和效率兼备。