Estimation of Fluctuation Characterizations by USV-Operation Simulations in Sea State 3

This paper proposes a method based on simulation techniques for fluctuation characterizations of unmanned surface vehicle (USV) operations under Sea State 3. In order to simulate the operations of a USV in Sea State 3, we generated the data of sea surfaces using linear wave theory and utilized the motion equation. Fluctuation analysis results by the proposed simulation method could provide crucial information for designing the stabilization system for the critical equipment on a USV. Through these works, it was verified that the design specifications such as range of motion, maximum speed, and acceleration could be estimated using the simulation data

Spatial Uncertainty Model for Visual Features Using a Kinect™ Sensor

This study proposes a mathematical uncertainty model for the spatial measurement of visual features using Kinect™ sensors. This model can provide qualitative and quantitative analysis for the utilization of Kinect™ sensors as 3D perception sensors. In order to achieve this objective, we derived the propagation relationship of the uncertainties between the disparity image space and the real Cartesian space with the mapping function between the two spaces. Using this propagation relationship, we obtained the mathematical model for the covariance matrix of the measurement error, which represents the uncertainty for spatial position of visual features from Kinect™ sensors. In order to derive the quantitative model of spatial uncertainty for visual features, we estimated the covariance matrix in the disparity image space using collected visual feature data. Further, we computed the spatial uncertainty information by applying the covariance matrix in the disparity image space and the calibrated sensor parameters to the proposed mathematical model. This spatial uncertainty model was verified by comparing the uncertainty ellipsoids for spatial covariance matrices and the distribution of scattered matching visual features. We expect that this spatial uncertainty model and its analyses will be useful in various Kinect™ sensor applications