Vision-based localization of intelligent vehicles is a research hot spot in the field of autonomous driving. However, in some scenes where the effective close-range features are not significant, it is easy to cause the decrease of the pose estimation accuracy due to the insufficient of features participating in the calculation. In order to solve the problem, this paper proposes a camera pose estimation method combined with lane detection algorithm. First, a set of lane detection algorithm based on adaptive region of interest selection and geometric structure filtering methods are designed to accurately detect the inside and outside lines of the left and right lanes.Then match the points in the lane area to between to frames to obtain extra matched-point-pairs. The ground disparity equation is fitted according to the V disparity map, through which the exact disparity values of the matching point pairs belonging to the lanes can be obtained. Finally, these matched-point-pairs are merged with the matching point pairs extracted by the ORB method to participate the pose calculation of camera. It is proved that the algorithm proposed in this paper improves the accuracy of pose estimation results. It does well in solving the pose estimation failure problem caused by insufficient effective feature points in some scenes with good environmental adaptability.