3차원 위치측정을 위한 스테레오 카메라 시스템의 인공 신경망을 이용한 보정 = Calibrating Stereoscopic 3D Position Measurement Systems Using Artificial Neural Nets

다국어 초록 (Multilingual Abstract)

Stereo cameras are the most widely used sensing systems for automated machines including robots to interact with their three-dimensional(3D) working environments. The position of a target point in the 3D world coordinates can be measured by the use of stereo cameras and the camera calibration is an important preliminary step for the task. Existing camera calibration techniques can be classified into two large categories - linear and nonlinear techniques. While linear techniques are simple but somewhat inaccurate, the nonlinear ones require a modeling process to compensate for the lens distortion and a rather complicated procedure to solve the nonlinear equations. In this paper, a method employing a neural network for the calibration problem is described for tackling the problems arisen when existing techniques are applied and the results are reported. Particularly, it is shown experimentally that by utilizing the function approximation capability of multi-layer neural networks trained by the back-propagation(BY) algorithm to learn the error pattern of a linear technique. the measurement accuracy can be aimply and efficiently increased.