Vision-based tracking with projective mapping for parameter identification of remotely operated vehicles

Abstract

Parameter identification of a remotely operated vehicle (ROV) is often based on the dynamic responses collected by its onboard sensors. However, for commercial ROVs, the required data for identification may not be available due to the absence of suitable sensors or limitations in accessing onboard sensor data. Therefore, this study proposes a vision-based tracking system to measure the dynamic response of an ROV. The tracking system is independent of the ROV, and is able to localize an ROV to a high degree of precision by means of projective mapping. The validity of the proposed tracking system is demonstrated through identification of a commercial ROV. A simplified nonlinear ROV dynamic model with six degrees of freedom (DOF) is used for identification. Uncoupled motions, including surge, sway, and yaw, are obtained from the ROV dynamic model, and the corresponding experiments are carried out for each DOF. Hydrodynamic parameters are then estimated with numerical optimizations by comparing the measured ROV responses with the output of the mathematical model. The experimental results show that the vision-based tracking system can accurately measure the transient and steady-state responses of an ROV. Additionally, the simulations of the ROV dynamic model, with the optimal parameter estimates, give results in agreement with the measured data.