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Underwater robots are subject to position drift due to the effect of oceancurrents and the lack of accurate localisation while submerged. We areinterested in exploiting such position drift to estimate the ocean current inthe surrounding area, thereby assisting navigation and planning. We present aGaussian process~(GP)-based expectation-maximisation~(EM) algorithm thatestimates the underlying ocean current using sparse GPS data obtained on thesurface and dead-reckoned position estimates. We first develop a specialised GPregression scheme that exploits the incompressibility of ocean currents tocounteract the underdetermined nature of the problem. We then use the proposedregression scheme in an EM algorithm that estimates the best-fitting oceancurrent in between each GPS fix. The proposed algorithm is validated insimulation and on a real dataset, and is shown to be capable of reconstructingthe underlying ocean current field. We expect to use this algorithm to closethe loop between planning and estimation for underwater navigation in unknownocean currents.

Online Estimation of Ocean Current from Sparse GPS Data for Underwater Vehicles