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In this paper, a stochastic optimization algorithm is proposed to calibrate the three-axis magnetometer onboard. The sensor errors, namely, hard iron, soft iron, nonorthogonality, scale factors, and bias, are taken into account. Particle swarm optimization (PSO) strategy is used to do the calibration, enhanced by the stretching technique to improve the accuracy and robustness. The performance of this algorithm is evaluated with a series of laboratory experiments and a field experiment of autonomous underwater vehicle. Comparisons with other analytical calibration methods are made. The results demonstrate that both the PSO and the stretched PSO algorithm can significantly compensate the magnetometer readings, and the latter algorithm has higher accuracy and more robustness. [ABSTRACT FROM PUBLISHER]

Optimization is often used to perform model calibration, the process of inferring the values of model parameters so that the results of the simulations best match observed behavior. It can both improve the predictive capability of the model and curtail the loss of information caused by using a numerical model instead of the actual system. At its heart is the comparison of experimental data and simulation results. Complicating this comparison is the fact that both data sets contain uncertainties which must be quantified in order to make reasonable comparisons. Therefore, uncertainty quantification (UQ) techniques can be applied to identify, characterize, reduce, and, if possible, eliminate uncertainties. Incorporation of UQ into the calibration process can drastically improve the usefulness of computational models. Current approaches are serial approaches in that first, the calibration parameters are identified and then, a series of runs dedicated to UQ analysis is completed. Although this approach can be effective,it can be computationally expensive or produce incomplete results. Model analysis that takes advantage of intermediate optimization iterates can reduce the expense, but the sampling done by the optimization algorithms is not ideal. In this paper, we will review serial approaches and propose a joint calibration and UQ approach that combines Bayesian statistical models and derivative-free optimization in order to monitor sensitivity information throughout the calibration process. [ABSTRACT FROM AUTHOR]

Abstract: A new approach to 3D scanners calibration, based on Fourier Transform Profilometry method, is proposed and verified by experiments. Commonly used calibration techniques require that a calibration plane is placed in different and known positions in the scanner measuring volume; an optimization procedure iteratively change the value of some calibration parameters in order to minimize the discrepancy between the height distributions of the measured planes and the reference ones. The main limit of this type of calibration techniques is the necessity to guarantee a high accuracy in the placement of the planes used as calibration references. The innovation of the method proposed in this paper is the complete removal of any device for the accurate plane placement by assigning the task of the plane positions estimation to the camera, which is part of the scanner. The well known camera calibration algorithm proposed by Zhang permits at the same time to calibrate the camera and to estimate the positions and orientations of the plane used for the calibration itself. The knowledge of the plane position allows to use them as a reference of the scanner calibration. The obtained results show that this method provides accuracy values for the scanner parameters estimation comparable with other calibration techniques, but the advantage is that no particular device is needed. [ABSTRACT FROM AUTHOR]