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# Calibration Methods

There are several calibration procedures available. The choice of these will depend on how the calibration data has been obtained and how much a-priori information needs to be taken into account.
• Tsai. This routine takes observed image locations and known 3D measuremets and fits a model to the left and right cameras separately using Tsai's calibration method (on grid data only). The technique does not determine image centres or aspect ratio's and does not deal with radial distortion effects. Though the algorithm can often be used to provide a good initial estimate it is neither optimal or robust and model optimisation should be finished using alternative techniques.

• IP min. This routine performs direct image plane minimisation of the difference between the observed and predicted locations of selected data. Left and right calibration is performed separately with respect to the specified "Model" camera parameters and the six parameters of camera transformation. The method used is an iterative minimisation technique which starts by choosing test values around an initial estimate according to the parameter "scale init" on the Calib Params menu. Convergence is determined by the Calib Params "c_test1" and "c_test2". The first parameter tests for convergence of a minimisation step, minimisation is restarted until the overall reduction in the error function is letts than the second parameter. The technique also excludes data which is inconsistent with the current calibration according to the value of the "accuracy" parameter. The technique is thus performing a robust fit which explicitly excludes outliers from the determination of the minimum.

• IS min. This routine performs direct image plane minimisation of the difference between the observed and predicted locations of selected data. Unlike the routine described above however, left and right cameras are calibrated simultaneously. This allows previously determined covariance estimates to be used to constrain the new solution in an optimal manner. Parameters are the same as for IP min.

• EPI min. This routine performs least-squares off epi-polar minimisation. The routine thus determines the left and right camera models which are most consistent with the observed stereo geometry. The absence of 3D information dictates that this method can only provide information on the ratio of the two focal lengths and the relative transformation between the camera co-ordinate systems up to a scale factor and this only whene there is a fair amount of perspective effects in the matched image data. A covariance estimate from a previous calibration must be used to constrain the minimisation process (see init covar below) when extra parameters are selected or the data is insufficient to uniquely determine the parameter set.

• Model min. This routine projects the features of a wireframe model (loaded in the Matcher Tool) from the view direction specified by the current calibration parameters. It then adjusts the parameters to get the maximum likelihood projection. This process is done for separate cameras and does not yet use the stereo camera covariance. It's main purpose is view and feature validation for automated model construction.

Next: Covariance Computation Up: Calib Tool Previous: Pre-Processing   Contents
root 2018-11-14