Camera calibration and three-dimensional world reconstruction of stereo-vision using neural networks

Stereo-pair images obtained from two cameras can be used to compute three-dimensional (3D) world coordinates of a point using triangulation. However, to apply this method, camera calibration parameters for each camera need to be experimentally obtained. Camera calibration is a rigorous experimental procedure in which typically 12 parameters are to be evaluated for each camera. The general camera model is often such that the system becomes nonlinear and requires good initial estimates to converge to a solution. We propose that, for stereo vision applications in which real-world coordinates are to be evaluated, artificial neural networks be used to train the system such that the need for camera calibration is eliminated. The training set for our neural network consists of a variety of stereo-pair images and corresponding 3D world coordinates. We present the results obtained on our prototype mobile robot that employs two cameras as its sole sensors and navigates through simple regular obstacles in a high-contrast environment. We observe that the percentage errors obtained from our set-up are comparable with those obtained through standard camera calibration techniques and that the system is accurate enough for most machine-vision applications.