Vision-based deck state estimation for autonomous ship-board landing

Benjamin L. Truskin, Jacob Willem Langelaan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper describes a method for ship deck estimation using only sensing carried aboard an autonomous rotorcraft: specifically, sensing is limited to a vision system, an inertial measurement unit and GPS. Using bearings to features on the ship deck and knowledge of helicopter state provided by the INS/GPS, a state estimator computes estimates of deck state and covariance. This deck state estimate can then be used to compute a safe, feasible trajectory to landing. This paper presents an Unscented Kalman Filter based implementation that uses a generic second order kinematic model driven by zero mean Gaussian noise for the ship deck motion model: while this deck motion model contains significant unmodeled dynamics it is not specific to a particular ship. Results of Monte Carlo simulations illustrate the utility of the proposed approach: good estimation results are obtained for stochastic deck motion (with a Pierson-Moskowitz power spectral density) and a fast ferry ship model.

Original languageEnglish (US)
Pages (from-to)1693-1711
Number of pages19
JournalAnnual Forum Proceedings - AHS International
Volume3
StatePublished - 2013

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State estimation
Landing
Ships
Global positioning system
Bearings (structural)
Ship models
Units of measurement
Power spectral density
Helicopters
Kalman filters
Kinematics
Trajectories

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

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Vision-based deck state estimation for autonomous ship-board landing. / Truskin, Benjamin L.; Langelaan, Jacob Willem.

In: Annual Forum Proceedings - AHS International, Vol. 3, 2013, p. 1693-1711.

Research output: Contribution to journalArticle

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