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 language | English (US) |
|---|---|
| Pages (from-to) | 1693-1711 |
| Number of pages | 19 |
| Journal | Annual Forum Proceedings - AHS International |
| Volume | 3 |
| State | Published - 2013 |
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All Science Journal Classification (ASJC) codes
- Engineering(all)
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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 journal › Article
TY - JOUR
T1 - Vision-based deck state estimation for autonomous ship-board landing
AU - Truskin, Benjamin L.
AU - Langelaan, Jacob Willem
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84883418508&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883418508&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84883418508
VL - 3
SP - 1693
EP - 1711
JO - Annual Forum Proceedings - AHS International
JF - Annual Forum Proceedings - AHS International
SN - 1552-2938
ER -