  In the autonomous ocean monitoring task, the sampling robot moves in the
environment and accumulates data continuously. The widely adopted spatial
modeling method - standard Gaussian process (GP) regression - becomes
inadequate in processing the growing sensing data of a large size. To overcome
the computational challenge, this paper presents an environmental modeling
framework using a sparse variant of GP called streaming sparse GP (SSGP). The
SSGP is able to handle streaming data in an online and incremental manner, and
is therefore suitable for long-term autonomous environmental monitoring. The
SSGP summarizes the collected data using a small set of pseudo data points that
best represent the whole dataset, and updates the hyperparameters and pseudo
point locations in a streaming fashion, leading to high-quality approximation
of the underlying environmental model with significantly reduced computational
cost and memory demand.
