Abstract: A new hybrid method to realise high-precision
distortion determination for optical ultra-precision 3D measurement
systems based on stereo cameras using active light projection is
introduced. It consists of two phases: the basic distortion
determination and the refinement. The refinement phase of the
procedure uses a plane surface and projected fringe patterns as
calibration tools to determine simultaneously the distortion of both
cameras within an iterative procedure. The new technique may be
performed in the state of the device “ready for measurement" which
avoids errors by a later adjustment. A considerable reduction of
distortion errors is achieved and leads to considerable improvements
of the accuracy of 3D measurements, especially in the precise
measurement of smooth surfaces.
Abstract: This paper presents a generalized form of the
mechanistic deconvolution technique (GMD) to modeling image sensors applicable in various pan–tilt planes of view. The mechanistic deconvolution technique (UMD) is modified with the
given angles of a pan–tilt plane of view to formulate constraint parameters and characterize distortion effects, and thereby, determine
the corrected image data. This, as a result, does not require experimental setup or calibration. Due to the mechanistic nature of
the sensor model, the necessity for the sensor image plane to be
orthogonal to its z-axis is eliminated, and it reduces the dependency on image data. An experiment was constructed to evaluate the
accuracy of a model created by GMD and its insensitivity to changes in sensor properties and in pan and tilt angles. This was compared
with a pre-calibrated model and a model created by UMD using two sensors with different specifications. It achieved similar accuracy
with one-seventh the number of iterations and attained lower mean error by a factor of 2.4 when compared to the pre-calibrated and
UMD model respectively. The model has also shown itself to be robust and, in comparison to pre-calibrated and UMD model, improved the accuracy significantly.