Joint calibration of Machine Vision subsystems for robuster surrounding 3D perception

To achieve successful autonomous navigation, a vision system that provides continuous and precise three-dimensional data of the system’s surroundings is always needed. An indispensable step in the acquisition of reliable data is the calibration of the system, preferably with a time-efficient and low-complexity approach. In this paper, a robust and efficient calibration method is proposed for the information fusion of a stereo vision system and a Technical Vision System. The proposed methodology achieves an error of x<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</inf> = 6.9401 mm, y<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</inf> = 8.0997 mm and z<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e</inf> = 15.1822 mm in 3.0202 seconds of processing time, as proven through experimental results.