The stereo vision system is one of the most important sensors for 3D reconstruction of the environment and scene understanding. It plays an essential role for the automated driving and ADAS applications. Although recently great progress has been made in this field both in the density and accuracy, the stereo vision in the challenging environment, such as rain, snow and low light conditions, are still open problems and need to be improved. To address this issue, we propose a novel real-time stereo vision algorithm, we transform the rough confidence score of the matching cost to the outlier probability rate and use it to guide the multi-path Viterbi method to estimate the disparity. Our proposed algorithm costs less than 40ms and improves the accuracy of stereo results in difficult scenarios. Real-world experiments show that the proposed method can significantly improve the disparity results in the various challenging environment.
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