Novel method of calibration with restrictive constraints for stereo-vision system

Abstract

Regarding the calibration of a stereo vision measurement system, this paper puts forward a new bundle adjustment algorithm based on the stereo vision camera calibration method. Multiple-view geometric constraints and a bundle adjustment algorithm are used to optimize the inner and outer parameters of the camera accurately. A fixed relative constraint relationship between cameras is introduced. We have improved the normal equation construction process of the traditional bundle adjustment method, so that each iteration process occurs just outside the parameters of two images that are taken by a camera that has been optimized to better integrate two cameras bound together as one camera. The relationship between the fixed relative constraints can effectively increase the number of superfluous observations of the adjustment system and optimize higher accuracy while reducing the dimension of the normal matrix; it means that each iteration will reduce the time required. Simulation and actual experimental results show the superior performance of the proposed approach in terms of robustness and accuracy, and our approach also can be extended to stereo-vision system with more than two cameras.