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

Abstract

Underwater robots are subject to position drift due to the effect of ocean currents and the lack of accurate localisation while submerged. We are interested in exploiting such position drift to estimate the ocean current in the surrounding area, thereby assisting navigation and planning. We present a Gaussian process (GP)-based expectation-maximisation (EM) algorithm that estimates the underlying ocean current using sparse GPS data obtained on the surface and dead-reckoned position estimates. We first develop a specialised GP regression scheme that exploits the incompressibility of ocean currents to counteract the underdetermined nature of the problem. We then use the proposed regression scheme in an EM algorithm that estimates the best-fitting ocean current in between each GPS fix. The proposed algorithm is validated in simulation and on a real dataset, and is shown to be capable of reconstructing the underlying ocean current field. We expect to use this algorithm to close the loop between planning and estimation for underwater navigation in unknown ocean currents.

3D Ensemble-Based Online Oceanic Flow Field Estimation for Underwater Glider Path Planning

“…In contrast to the above model-based techniques, some data-driven techniques have also previously been used for flow field estimation. The "incompressible Gaussian Process (GP)" was introduced in [14], where a novel kernel was developed to enforce incompressibility of 2D flow field estimates. Another related method, the kernel observer, combines a kernel embedding with an observer such as a Kalman filter [15][16][17].…”

“…Kernel methods are a common machine learning tool to allow linear methods to be applied to nonlinear patterns in data, and have previously been used to represent flow fields [3,14,15]. Kernels can be used to build a continuous spatial model of the flow field from data samples at discrete locations.…”

“…Kernels can be used to build a continuous spatial model of the flow field from data samples at discrete locations. The incompressible kernel allows this "interpolation" to respect incompressibility [14] in 2D; in this work we apply the incompressible kernel to describe 3D oceanic flow fields.…”

Distance and Steering Heuristics for Streamline-Based Flow Field Planning

“…Oceanic current predictions are freely available [19][20][21], but may have uncertainties that are significant when planning for slow-moving vehicles. In previous work we successfully used drift errors to estimate a local stream function online [22].…”

Streamlines for Motion Planning in Underwater Currents

“…We have designed our method to be easily integrated with other sampling-based algorithms. Implementing FMT * or BIT * [30], for example, would be interesting avenues to pursue, in addition to performing field experiments using flow estimations methods described in [31] with gliders and other types of AUVs. Beyond motion planning, the proposed method can be used in task planning for vehicles in flow fields [32,33], which would benefit from the reduced complexity.…”