Estimation of Spatially-Correlated Ocean Currents from Ensemble Forecasts and Online Measurements

To, K. Y. Cadmus; Kong, Felix H.; Lee, Ki Myung Brian; Yoo, Chanyeol; Anstee, Stuart

doi:10.1109/icra48506.2021.9561544

Cited by 13 publications

(16 citation statements)

References 23 publications

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“…Results illuminated clear limitations involving placement of sparse GP inducing points which will be addressed in future work via online updates to inducing point locations. Further work also lies in extensions for reconstruction of time-varying and multi-dimensional spatial fields [14,[32][33][34][35].…”

Section: Discussionmentioning

confidence: 99%

“…One such approximation is the inducing points formulation [9], where the target function is assumed to be conditionally independent given the function values at a fixed set of inducing points. A recent advance in this direction is that incoming sensor measurements can be 'fused' via recursive Bayesian estimation of a latent Gaussian state of fixed dimensionality, reminiscent of Kalman filtering [3,7,[13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

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Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression

Jennifer¹,

Lee²,

Yoo³

et al. 2022

Preprint

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We present a planning framework for minimising the deterministic worst-case error in sparse Gaussian process (GP) regression. We first derive a universal worst-case error bound for sparse GP regression with bounded noise using interpolation theory on reproducing kernel Hilbert spaces (RKHSs). By exploiting the conditional independence (CI) assumption central to sparse GP regression, we show that the worst-case error minimisation can be achieved by solving a posterior entropy minimisation problem. In turn, the posterior entropy minimisation problem is solved using a Gaussian belief space planning algorithm. We corroborate the proposed worst-case error bound in a simple 1D example, and test the planning framework in simulation for a 2D vehicle in a complex flow field. Our results demonstrate that the proposed posterior entropy minimisation approach is effective in minimising deterministic error, and outperforms the conventional measurement entropy maximisation formulation when the inducing points are fixed.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression

Jennifer¹,

Lee²,

Yoo³

et al. 2022

Preprint

Self Cite

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show abstract

“…Departure location (at surface) marked by an 'x', target destination (at 500m depth) marked by circle. field estimate in 2D [3]. However, for the purpose of pathplanning for underwater gliders, which oscillate through a wide depth range, 2D estimates appear to be insufficient.…”

Section: Introductionmentioning

confidence: 99%

“…Our existing algorithm [3] attempts to estimate an unknown 2D flow field as a linear combination of 2D "basis flow fields" generated from the ensemble forecast. The direct extension to the 3D case would result in 3D basis flow fields, which are limited to the span of the ensemble forecast.…”

Section: Introductionmentioning

confidence: 99%

“…By breaking them apart into 2D flow fields and sharing these 2D flow fields between depths, the number of degrees of freedom increase, and greater expressivity of the basis is achieved. The contribution of this paper is to not only to simply extend [3] to 3D, but to adjust the "naive" 3D extension using the fact of negligible vertical velocity in order to increase the span of the basis, and hence improve estimation accuracy.…”

Section: Introductionmentioning

confidence: 99%

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

Kong¹,

To²,

Brassington³

et al. 2021

2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Estimating ocean flow fields in 3D is a critical step in enabling the reliable operation of underwater gliders and other small, low-powered autonomous marine vehicles. Existing methods produce depth-averaged 2D layers arranged at discrete vertical intervals, but this type of estimation can lead to severe navigation errors. Based on the observation that real-world ocean currents exhibit relatively low vertical velocity components, we propose an accurate 3D estimator that extends our previous work in estimating 2D flow fields as a linear combination of basis flows. The proposed algorithm uses data from ensemble forecasting to build a set of 3D basis flows, and then iteratively updates basis coefficients using point measurements of underwater currents. We report results from experiments using actual ensemble forecasts and synthetic measurements to compare the performance of our method to the direct 3D extension of the previous work. These results show that our method produces estimates with dramatically lower error metrics, with and without measurement noise.

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Optimization-Based Online Flow Fields Estimation for AUVs Navigation

Xu¹,

Lu²,

Pan³

2023

Springer Proceedings in Advanced Robotics

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Estimation of Spatially-Correlated Ocean Currents from Ensemble Forecasts and Online Measurements

Cited by 13 publications

References 23 publications

Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression

Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression

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

Optimization-Based Online Flow Fields Estimation for AUVs Navigation

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